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Limitations in Research – Types, Examples and Writing Guide

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Limitations in Research

Limitations in research refer to the factors that may affect the results, conclusions , and generalizability of a study. These limitations can arise from various sources, such as the design of the study, the sampling methods used, the measurement tools employed, and the limitations of the data analysis techniques.

Types of Limitations in Research

Types of Limitations in Research are as follows:

Sample Size Limitations

This refers to the size of the group of people or subjects that are being studied. If the sample size is too small, then the results may not be representative of the population being studied. This can lead to a lack of generalizability of the results.

Time Limitations

Time limitations can be a constraint on the research process . This could mean that the study is unable to be conducted for a long enough period of time to observe the long-term effects of an intervention, or to collect enough data to draw accurate conclusions.

Selection Bias

This refers to a type of bias that can occur when the selection of participants in a study is not random. This can lead to a biased sample that is not representative of the population being studied.

Confounding Variables

Confounding variables are factors that can influence the outcome of a study, but are not being measured or controlled for. These can lead to inaccurate conclusions or a lack of clarity in the results.

Measurement Error

This refers to inaccuracies in the measurement of variables, such as using a faulty instrument or scale. This can lead to inaccurate results or a lack of validity in the study.

Ethical Limitations

Ethical limitations refer to the ethical constraints placed on research studies. For example, certain studies may not be allowed to be conducted due to ethical concerns, such as studies that involve harm to participants.

Examples of Limitations in Research

Some Examples of Limitations in Research are as follows:

Research Title: “The Effectiveness of Machine Learning Algorithms in Predicting Customer Behavior”

Limitations:

The study only considered a limited number of machine learning algorithms and did not explore the effectiveness of other algorithms.
The study used a specific dataset, which may not be representative of all customer behaviors or demographics.
The study did not consider the potential ethical implications of using machine learning algorithms in predicting customer behavior.

Research Title: “The Impact of Online Learning on Student Performance in Computer Science Courses”

The study was conducted during the COVID-19 pandemic, which may have affected the results due to the unique circumstances of remote learning.
The study only included students from a single university, which may limit the generalizability of the findings to other institutions.
The study did not consider the impact of individual differences, such as prior knowledge or motivation, on student performance in online learning environments.

Research Title: “The Effect of Gamification on User Engagement in Mobile Health Applications”

The study only tested a specific gamification strategy and did not explore the effectiveness of other gamification techniques.
The study relied on self-reported measures of user engagement, which may be subject to social desirability bias or measurement errors.
The study only included a specific demographic group (e.g., young adults) and may not be generalizable to other populations with different preferences or needs.

How to Write Limitations in Research

When writing about the limitations of a research study, it is important to be honest and clear about the potential weaknesses of your work. Here are some tips for writing about limitations in research:

Identify the limitations: Start by identifying the potential limitations of your research. These may include sample size, selection bias, measurement error, or other issues that could affect the validity and reliability of your findings.
Be honest and objective: When describing the limitations of your research, be honest and objective. Do not try to minimize or downplay the limitations, but also do not exaggerate them. Be clear and concise in your description of the limitations.
Provide context: It is important to provide context for the limitations of your research. For example, if your sample size was small, explain why this was the case and how it may have affected your results. Providing context can help readers understand the limitations in a broader context.
Discuss implications : Discuss the implications of the limitations for your research findings. For example, if there was a selection bias in your sample, explain how this may have affected the generalizability of your findings. This can help readers understand the limitations in terms of their impact on the overall validity of your research.
Provide suggestions for future research : Finally, provide suggestions for future research that can address the limitations of your study. This can help readers understand how your research fits into the broader field and can provide a roadmap for future studies.

Purpose of Limitations in Research

There are several purposes of limitations in research. Here are some of the most important ones:

To acknowledge the boundaries of the study : Limitations help to define the scope of the research project and set realistic expectations for the findings. They can help to clarify what the study is not intended to address.
To identify potential sources of bias: Limitations can help researchers identify potential sources of bias in their research design, data collection, or analysis. This can help to improve the validity and reliability of the findings.
To provide opportunities for future research: Limitations can highlight areas for future research and suggest avenues for further exploration. This can help to advance knowledge in a particular field.
To demonstrate transparency and accountability: By acknowledging the limitations of their research, researchers can demonstrate transparency and accountability to their readers, peers, and funders. This can help to build trust and credibility in the research community.
To encourage critical thinking: Limitations can encourage readers to critically evaluate the study’s findings and consider alternative explanations or interpretations. This can help to promote a more nuanced and sophisticated understanding of the topic under investigation.

When to Write Limitations in Research

Limitations should be included in research when they help to provide a more complete understanding of the study’s results and implications. A limitation is any factor that could potentially impact the accuracy, reliability, or generalizability of the study’s findings.

It is important to identify and discuss limitations in research because doing so helps to ensure that the results are interpreted appropriately and that any conclusions drawn are supported by the available evidence. Limitations can also suggest areas for future research, highlight potential biases or confounding factors that may have affected the results, and provide context for the study’s findings.

Generally, limitations should be discussed in the conclusion section of a research paper or thesis, although they may also be mentioned in other sections, such as the introduction or methods. The specific limitations that are discussed will depend on the nature of the study, the research question being investigated, and the data that was collected.

Examples of limitations that might be discussed in research include sample size limitations, data collection methods, the validity and reliability of measures used, and potential biases or confounding factors that could have affected the results. It is important to note that limitations should not be used as a justification for poor research design or methodology, but rather as a way to enhance the understanding and interpretation of the study’s findings.

Importance of Limitations in Research

Here are some reasons why limitations are important in research:

Enhances the credibility of research: Limitations highlight the potential weaknesses and threats to validity, which helps readers to understand the scope and boundaries of the study. This improves the credibility of research by acknowledging its limitations and providing a clear picture of what can and cannot be concluded from the study.
Facilitates replication: By highlighting the limitations, researchers can provide detailed information about the study’s methodology, data collection, and analysis. This information helps other researchers to replicate the study and test the validity of the findings, which enhances the reliability of research.
Guides future research : Limitations provide insights into areas for future research by identifying gaps or areas that require further investigation. This can help researchers to design more comprehensive and effective studies that build on existing knowledge.
Provides a balanced view: Limitations help to provide a balanced view of the research by highlighting both strengths and weaknesses. This ensures that readers have a clear understanding of the study’s limitations and can make informed decisions about the generalizability and applicability of the findings.

Advantages of Limitations in Research

Here are some potential advantages of limitations in research:

Focus : Limitations can help researchers focus their study on a specific area or population, which can make the research more relevant and useful.
Realism : Limitations can make a study more realistic by reflecting the practical constraints and challenges of conducting research in the real world.
Innovation : Limitations can spur researchers to be more innovative and creative in their research design and methodology, as they search for ways to work around the limitations.
Rigor : Limitations can actually increase the rigor and credibility of a study, as researchers are forced to carefully consider the potential sources of bias and error, and address them to the best of their abilities.
Generalizability : Limitations can actually improve the generalizability of a study by ensuring that it is not overly focused on a specific sample or situation, and that the results can be applied more broadly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write Limitations of the Study (with examples)

This blog emphasizes the importance of recognizing and effectively writing about limitations in research. It discusses the types of limitations, their significance, and provides guidelines for writing about them, highlighting their role in advancing scholarly research.

Updated on August 24, 2023

a group of researchers writing their limitation of their study

No matter how well thought out, every research endeavor encounters challenges. There is simply no way to predict all possible variances throughout the process.

These uncharted boundaries and abrupt constraints are known as limitations in research . Identifying and acknowledging limitations is crucial for conducting rigorous studies. Limitations provide context and shed light on gaps in the prevailing inquiry and literature.

This article explores the importance of recognizing limitations and discusses how to write them effectively. By interpreting limitations in research and considering prevalent examples, we aim to reframe the perception from shameful mistakes to respectable revelations.

What are limitations in research?

In the clearest terms, research limitations are the practical or theoretical shortcomings of a study that are often outside of the researcher’s control . While these weaknesses limit the generalizability of a study’s conclusions, they also present a foundation for future research.

Sometimes limitations arise from tangible circumstances like time and funding constraints, or equipment and participant availability. Other times the rationale is more obscure and buried within the research design. Common types of limitations and their ramifications include:

Theoretical: limits the scope, depth, or applicability of a study.
Methodological: limits the quality, quantity, or diversity of the data.
Empirical: limits the representativeness, validity, or reliability of the data.
Analytical: limits the accuracy, completeness, or significance of the findings.
Ethical: limits the access, consent, or confidentiality of the data.

Regardless of how, when, or why they arise, limitations are a natural part of the research process and should never be ignored . Like all other aspects, they are vital in their own purpose.

Why is identifying limitations important?

Whether to seek acceptance or avoid struggle, humans often instinctively hide flaws and mistakes. Merging this thought process into research by attempting to hide limitations, however, is a bad idea. It has the potential to negate the validity of outcomes and damage the reputation of scholars.

By identifying and addressing limitations throughout a project, researchers strengthen their arguments and curtail the chance of peer censure based on overlooked mistakes. Pointing out these flaws shows an understanding of variable limits and a scrupulous research process.

Showing awareness of and taking responsibility for a project’s boundaries and challenges validates the integrity and transparency of a researcher. It further demonstrates the researchers understand the applicable literature and have thoroughly evaluated their chosen research methods.

Presenting limitations also benefits the readers by providing context for research findings. It guides them to interpret the project’s conclusions only within the scope of very specific conditions. By allowing for an appropriate generalization of the findings that is accurately confined by research boundaries and is not too broad, limitations boost a study’s credibility .

Limitations are true assets to the research process. They highlight opportunities for future research. When researchers identify the limitations of their particular approach to a study question, they enable precise transferability and improve chances for reproducibility.

Simply stating a project’s limitations is not adequate for spurring further research, though. To spark the interest of other researchers, these acknowledgements must come with thorough explanations regarding how the limitations affected the current study and how they can potentially be overcome with amended methods.

How to write limitations

Typically, the information about a study’s limitations is situated either at the beginning of the discussion section to provide context for readers or at the conclusion of the discussion section to acknowledge the need for further research. However, it varies depending upon the target journal or publication guidelines.

Don’t hide your limitations

It is also important to not bury a limitation in the body of the paper unless it has a unique connection to a topic in that section. If so, it needs to be reiterated with the other limitations or at the conclusion of the discussion section. Wherever it is included in the manuscript, ensure that the limitations section is prominently positioned and clearly introduced.

While maintaining transparency by disclosing limitations means taking a comprehensive approach, it is not necessary to discuss everything that could have potentially gone wrong during the research study. If there is no commitment to investigation in the introduction, it is unnecessary to consider the issue a limitation to the research. Wholly consider the term ‘limitations’ and ask, “Did it significantly change or limit the possible outcomes?” Then, qualify the occurrence as either a limitation to include in the current manuscript or as an idea to note for other projects.

Writing limitations

Once the limitations are concretely identified and it is decided where they will be included in the paper, researchers are ready for the writing task. Including only what is pertinent, keeping explanations detailed but concise, and employing the following guidelines is key for crafting valuable limitations:

1) Identify and describe the limitations : Clearly introduce the limitation by classifying its form and specifying its origin. For example:

An unintentional bias encountered during data collection
An intentional use of unplanned post-hoc data analysis

2) Explain the implications : Describe how the limitation potentially influences the study’s findings and how the validity and generalizability are subsequently impacted. Provide examples and evidence to support claims of the limitations’ effects without making excuses or exaggerating their impact. Overall, be transparent and objective in presenting the limitations, without undermining the significance of the research.

3) Provide alternative approaches for future studies : Offer specific suggestions for potential improvements or avenues for further investigation. Demonstrate a proactive approach by encouraging future research that addresses the identified gaps and, therefore, expands the knowledge base.

Whether presenting limitations as an individual section within the manuscript or as a subtopic in the discussion area, authors should use clear headings and straightforward language to facilitate readability. There is no need to complicate limitations with jargon, computations, or complex datasets.

Examples of common limitations

Limitations are generally grouped into two categories , methodology and research process .

Methodology limitations

Methodology may include limitations due to:

Sample size
Lack of available or reliable data
Lack of prior research studies on the topic
Measure used to collect the data
Self-reported data

The researcher is addressing how the large sample size requires a reassessment of the measures used to collect and analyze the data.

Research process limitations

Limitations during the research process may arise from:

Access to information
Longitudinal effects
Cultural and other biases
Language fluency
Time constraints

The author is pointing out that the model’s estimates are based on potentially biased observational studies.

Final thoughts

Successfully proving theories and touting great achievements are only two very narrow goals of scholarly research. The true passion and greatest efforts of researchers comes more in the form of confronting assumptions and exploring the obscure.

In many ways, recognizing and sharing the limitations of a research study both allows for and encourages this type of discovery that continuously pushes research forward. By using limitations to provide a transparent account of the project's boundaries and to contextualize the findings, researchers pave the way for even more robust and impactful research in the future.

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The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. Study limitations are the constraints placed on the ability to generalize from the results, to further describe applications to practice, and/or related to the utility of findings that are the result of the ways in which you initially chose to design the study or the method used to establish internal and external validity or the result of unanticipated challenges that emerged during the study.

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Theofanidis, Dimitrios and Antigoni Fountouki. "Limitations and Delimitations in the Research Process." Perioperative Nursing 7 (September-December 2018): 155-163. .

Importance of...

Always acknowledge a study's limitations. It is far better that you identify and acknowledge your study’s limitations than to have them pointed out by your professor and have your grade lowered because you appeared to have ignored them or didn't realize they existed.

Keep in mind that acknowledgment of a study's limitations is an opportunity to make suggestions for further research. If you do connect your study's limitations to suggestions for further research, be sure to explain the ways in which these unanswered questions may become more focused because of your study.

Acknowledgment of a study's limitations also provides you with opportunities to demonstrate that you have thought critically about the research problem, understood the relevant literature published about it, and correctly assessed the methods chosen for studying the problem. A key objective of the research process is not only discovering new knowledge but also to confront assumptions and explore what we don't know.

Claiming limitations is a subjective process because you must evaluate the impact of those limitations . Don't just list key weaknesses and the magnitude of a study's limitations. To do so diminishes the validity of your research because it leaves the reader wondering whether, or in what ways, limitation(s) in your study may have impacted the results and conclusions. Limitations require a critical, overall appraisal and interpretation of their impact. You should answer the question: do these problems with errors, methods, validity, etc. eventually matter and, if so, to what extent?

Price, James H. and Judy Murnan. “Research Limitations and the Necessity of Reporting Them.” American Journal of Health Education 35 (2004): 66-67; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com.

Descriptions of Possible Limitations

All studies have limitations . However, it is important that you restrict your discussion to limitations related to the research problem under investigation. For example, if a meta-analysis of existing literature is not a stated purpose of your research, it should not be discussed as a limitation. Do not apologize for not addressing issues that you did not promise to investigate in the introduction of your paper.

Here are examples of limitations related to methodology and the research process you may need to describe and discuss how they possibly impacted your results. Note that descriptions of limitations should be stated in the past tense because they were discovered after you completed your research.

Possible Methodological Limitations

Sample size -- the number of the units of analysis you use in your study is dictated by the type of research problem you are investigating. Note that, if your sample size is too small, it will be difficult to find significant relationships from the data, as statistical tests normally require a larger sample size to ensure a representative distribution of the population and to be considered representative of groups of people to whom results will be generalized or transferred. Note that sample size is generally less relevant in qualitative research if explained in the context of the research problem.
Lack of available and/or reliable data -- a lack of data or of reliable data will likely require you to limit the scope of your analysis, the size of your sample, or it can be a significant obstacle in finding a trend and a meaningful relationship. You need to not only describe these limitations but provide cogent reasons why you believe data is missing or is unreliable. However, don’t just throw up your hands in frustration; use this as an opportunity to describe a need for future research based on designing a different method for gathering data.
Lack of prior research studies on the topic -- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the research problem you are investigating. Depending on the currency or scope of your research topic, there may be little, if any, prior research on your topic. Before assuming this to be true, though, consult with a librarian! In cases when a librarian has confirmed that there is little or no prior research, you may be required to develop an entirely new research typology [for example, using an exploratory rather than an explanatory research design ]. Note again that discovering a limitation can serve as an important opportunity to identify new gaps in the literature and to describe the need for further research.
Measure used to collect the data -- sometimes it is the case that, after completing your interpretation of the findings, you discover that the way in which you gathered data inhibited your ability to conduct a thorough analysis of the results. For example, you regret not including a specific question in a survey that, in retrospect, could have helped address a particular issue that emerged later in the study. Acknowledge the deficiency by stating a need for future researchers to revise the specific method for gathering data.
Self-reported data -- whether you are relying on pre-existing data or you are conducting a qualitative research study and gathering the data yourself, self-reported data is limited by the fact that it rarely can be independently verified. In other words, you have to the accuracy of what people say, whether in interviews, focus groups, or on questionnaires, at face value. However, self-reported data can contain several potential sources of bias that you should be alert to and note as limitations. These biases become apparent if they are incongruent with data from other sources. These are: (1) selective memory [remembering or not remembering experiences or events that occurred at some point in the past]; (2) telescoping [recalling events that occurred at one time as if they occurred at another time]; (3) attribution [the act of attributing positive events and outcomes to one's own agency, but attributing negative events and outcomes to external forces]; and, (4) exaggeration [the act of representing outcomes or embellishing events as more significant than is actually suggested from other data].

Possible Limitations of the Researcher

Access -- if your study depends on having access to people, organizations, data, or documents and, for whatever reason, access is denied or limited in some way, the reasons for this needs to be described. Also, include an explanation why being denied or limited access did not prevent you from following through on your study.
Longitudinal effects -- unlike your professor, who can literally devote years [even a lifetime] to studying a single topic, the time available to investigate a research problem and to measure change or stability over time is constrained by the due date of your assignment. Be sure to choose a research problem that does not require an excessive amount of time to complete the literature review, apply the methodology, and gather and interpret the results. If you're unsure whether you can complete your research within the confines of the assignment's due date, talk to your professor.
Cultural and other type of bias -- we all have biases, whether we are conscience of them or not. Bias is when a person, place, event, or thing is viewed or shown in a consistently inaccurate way. Bias is usually negative, though one can have a positive bias as well, especially if that bias reflects your reliance on research that only support your hypothesis. When proof-reading your paper, be especially critical in reviewing how you have stated a problem, selected the data to be studied, what may have been omitted, the manner in which you have ordered events, people, or places, how you have chosen to represent a person, place, or thing, to name a phenomenon, or to use possible words with a positive or negative connotation. NOTE : If you detect bias in prior research, it must be acknowledged and you should explain what measures were taken to avoid perpetuating that bias. For example, if a previous study only used boys to examine how music education supports effective math skills, describe how your research expands the study to include girls.
Fluency in a language -- if your research focuses , for example, on measuring the perceived value of after-school tutoring among Mexican-American ESL [English as a Second Language] students and you are not fluent in Spanish, you are limited in being able to read and interpret Spanish language research studies on the topic or to speak with these students in their primary language. This deficiency should be acknowledged.

Structure and Writing Style

Information about the limitations of your study are generally placed either at the beginning of the discussion section of your paper so the reader knows and understands the limitations before reading the rest of your analysis of the findings, or, the limitations are outlined at the conclusion of the discussion section as an acknowledgement of the need for further study. Statements about a study's limitations should not be buried in the body [middle] of the discussion section unless a limitation is specific to something covered in that part of the paper. If this is the case, though, the limitation should be reiterated at the conclusion of the section.

If you determine that your study is seriously flawed due to important limitations , such as, an inability to acquire critical data, consider reframing it as an exploratory study intended to lay the groundwork for a more complete research study in the future. Be sure, though, to specifically explain the ways that these flaws can be successfully overcome in a new study.

But, do not use this as an excuse for not developing a thorough research paper! Review the tab in this guide for developing a research topic . If serious limitations exist, it generally indicates a likelihood that your research problem is too narrowly defined or that the issue or event under study is too recent and, thus, very little research has been written about it. If serious limitations do emerge, consult with your professor about possible ways to overcome them or how to revise your study.

When discussing the limitations of your research, be sure to:

Describe each limitation in detailed but concise terms;
Explain why each limitation exists;
Provide the reasons why each limitation could not be overcome using the method(s) chosen to acquire or gather the data [cite to other studies that had similar problems when possible];
Assess the impact of each limitation in relation to the overall findings and conclusions of your study; and,
If appropriate, describe how these limitations could point to the need for further research.

Remember that the method you chose may be the source of a significant limitation that has emerged during your interpretation of the results [for example, you didn't interview a group of people that you later wish you had]. If this is the case, don't panic. Acknowledge it, and explain how applying a different or more robust methodology might address the research problem more effectively in a future study. A underlying goal of scholarly research is not only to show what works, but to demonstrate what doesn't work or what needs further clarification.

Aguinis, Hermam and Jeffrey R. Edwards. “Methodological Wishes for the Next Decade and How to Make Wishes Come True.” Journal of Management Studies 51 (January 2014): 143-174; Brutus, Stéphane et al. "Self-Reported Limitations and Future Directions in Scholarly Reports: Analysis and Recommendations." Journal of Management 39 (January 2013): 48-75; Ioannidis, John P.A. "Limitations are not Properly Acknowledged in the Scientific Literature." Journal of Clinical Epidemiology 60 (2007): 324-329; Pasek, Josh. Writing the Empirical Social Science Research Paper: A Guide for the Perplexed. January 24, 2012. Academia.edu; Structure: How to Structure the Research Limitations Section of Your Dissertation. Dissertations and Theses: An Online Textbook. Laerd.com; What Is an Academic Paper? Institute for Writing Rhetoric. Dartmouth College; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

Writing Tip

Don't Inflate the Importance of Your Findings!

After all the hard work and long hours devoted to writing your research paper, it is easy to get carried away with attributing unwarranted importance to what you’ve done. We all want our academic work to be viewed as excellent and worthy of a good grade, but it is important that you understand and openly acknowledge the limitations of your study. Inflating the importance of your study's findings could be perceived by your readers as an attempt hide its flaws or encourage a biased interpretation of the results. A small measure of humility goes a long way!

Another Writing Tip

Negative Results are Not a Limitation!

Negative evidence refers to findings that unexpectedly challenge rather than support your hypothesis. If you didn't get the results you anticipated, it may mean your hypothesis was incorrect and needs to be reformulated. Or, perhaps you have stumbled onto something unexpected that warrants further study. Moreover, the absence of an effect may be very telling in many situations, particularly in experimental research designs. In any case, your results may very well be of importance to others even though they did not support your hypothesis. Do not fall into the trap of thinking that results contrary to what you expected is a limitation to your study. If you carried out the research well, they are simply your results and only require additional interpretation.

Lewis, George H. and Jonathan F. Lewis. “The Dog in the Night-Time: Negative Evidence in Social Research.” The British Journal of Sociology 31 (December 1980): 544-558.

Yet Another Writing Tip

Sample Size Limitations in Qualitative Research

Sample sizes are typically smaller in qualitative research because, as the study goes on, acquiring more data does not necessarily lead to more information. This is because one occurrence of a piece of data, or a code, is all that is necessary to ensure that it becomes part of the analysis framework. However, it remains true that sample sizes that are too small cannot adequately support claims of having achieved valid conclusions and sample sizes that are too large do not permit the deep, naturalistic, and inductive analysis that defines qualitative inquiry. Determining adequate sample size in qualitative research is ultimately a matter of judgment and experience in evaluating the quality of the information collected against the uses to which it will be applied and the particular research method and purposeful sampling strategy employed. If the sample size is found to be a limitation, it may reflect your judgment about the methodological technique chosen [e.g., single life history study versus focus group interviews] rather than the number of respondents used.

Boddy, Clive Roland. "Sample Size for Qualitative Research." Qualitative Market Research: An International Journal 19 (2016): 426-432; Huberman, A. Michael and Matthew B. Miles. "Data Management and Analysis Methods." In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 428-444; Blaikie, Norman. "Confounding Issues Related to Determining Sample Size in Qualitative Research." International Journal of Social Research Methodology 21 (2018): 635-641; Oppong, Steward Harrison. "The Problem of Sampling in qualitative Research." Asian Journal of Management Sciences and Education 2 (2013): 202-210.

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What are the limitations in research and how to write them?

Learn about the potential limitations in research and how to appropriately address them in order to deliver honest and ethical research.

It is fairly uncommon for researchers to stumble into the term research limitations when working on their research paper. Limitations in research can arise owing to constraints on design, methods, materials, and so on, and these aspects, unfortunately, may have an influence on your subject’s findings.

In this Mind The Graph’s article, we’ll discuss some recommendations for writing limitations in research , provide examples of various common types of limitations, and suggest how to properly present this information.

What are the limitations in research?

The limitations in research are the constraints in design, methods or even researchers’ limitations that affect and influence the interpretation of your research’s ultimate findings. These are limitations on the generalization and usability of findings that emerge from the design of the research and/or the method employed to ensure validity both internally and externally.

Researchers are usually cautious to acknowledge the limitations of their research in their publications for fear of undermining the research’s scientific validity. No research is faultless or covers every possible angle. As a result, addressing the constraints of your research exhibits honesty and integrity .

Why should include limitations of research in my paper?

Though limitations tackle potential flaws in research, commenting on them at the conclusion of your paper, by demonstrating that you are aware of these limitations and explaining how they impact the conclusions that may be taken from the research, improves your research by disclosing any issues before other researchers or reviewers do .

Additionally, emphasizing research constraints implies that you have thoroughly investigated the ramifications of research shortcomings and have a thorough understanding of your research problem.

Limits exist in any research; being honest about them and explaining them would impress researchers and reviewers more than disregarding them.

Remember that acknowledging a research’s shortcomings offers a chance to provide ideas for future research, but be careful to describe how your study may help to concentrate on these outstanding problems.

Possible limitations examples

Here are some limitations connected to methodology and the research procedure that you may need to explain and discuss in connection to your findings.

Methodological limitations

Sample size.

The number of units of analysis used in your study is determined by the sort of research issue being investigated. It is important to note that if your sample is too small, finding significant connections in the data will be challenging, as statistical tests typically require a larger sample size to ensure a fair representation and this can be limiting.

Lack of available or reliable data

A lack of data or trustworthy data will almost certainly necessitate limiting the scope of your research or the size of your sample, or it can be a substantial impediment to identifying a pattern and a relevant connection.

Lack of prior research on the subject

Citing previous research papers forms the basis of your literature review and aids in comprehending the research subject you are researching. Yet there may be little if any, past research on your issue.

The measure used to collect data

After finishing your analysis of the findings, you realize that the method you used to collect data limited your capacity to undertake a comprehensive evaluation of the findings. Recognize the flaw by mentioning that future researchers should change the specific approach for data collection.

Issues with research samples and selection

Sampling inaccuracies arise when a probability sampling method is employed to choose a sample, but that sample does not accurately represent the overall population or the relevant group. As a result, your study suffers from “sampling bias” or “selection bias.”

Limitations of the research

When your research requires polling certain persons or a specific group, you may have encountered the issue of limited access to these interviewees. Because of the limited access, you may need to reorganize or rearrange your research. In this scenario, explain why access is restricted and ensure that your findings are still trustworthy and valid despite the constraint.

Time constraints

Practical difficulties may limit the amount of time available to explore a research issue and monitor changes as they occur. If time restrictions have any detrimental influence on your research, recognize this impact by expressing the necessity for a future investigation.

Due to their cultural origins or opinions on observed events, researchers may carry biased opinions, which can influence the credibility of a research. Furthermore, researchers may exhibit biases toward data and conclusions that only support their hypotheses or arguments.

The structure of the limitations section

The limitations of your research are usually stated at the beginning of the discussion section of your paper so that the reader is aware of and comprehends the limitations prior to actually reading the rest of your findings, or they are stated at the end of the discussion section as an acknowledgment of the need for further research.

The ideal way is to divide your limitations section into three steps:

1. Identify the research constraints;

2. Describe in great detail how they affect your research;

3. Mention the opportunity for future investigations and give possibilities.

By following this method while addressing the constraints of your research, you will be able to effectively highlight your research’s shortcomings without jeopardizing the quality and integrity of your research.

Present your research or paper in an innovative way

If you want your readers to be engaged and participate in your research, try Mind The Graph tool to add visual assets to your content. Infographics may improve comprehension and are easy to read, just as the Mind The Graph tool is simple to use and offers a variety of templates from which you can select the one that best suits your information.

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Research Limitations: A Comprehensive Guide

Embarking on a research journey is an exciting endeavor, but every study has its boundaries and constraints. Understanding and transparently acknowledging these limitations is a crucial aspect of scholarly work. In this guide, we'll explore the concept of research limitations, why they matter, and how to effectively address and navigate them in your academic endeavors.

1. Defining Research Limitations:

Definition: Research limitations are the constraints or shortcomings that affect the scope, applicability, and generalizability of a study.
Inherent in Research: Every research project, regardless of its scale or significance, possesses limitations.

2. Types of Research Limitations:

Methodological Limitations: Constraints related to the research design, data collection methods, or analytical techniques.
Sampling Limitations: Issues associated with the representativeness or size of the study sample.
Contextual Limitations: Restrictions stemming from the specific time, place, or cultural context of the study.
Resource Limitations: Constraints related to time, budget, or access to necessary resources.

3. Why Acknowledge Limitations?

Transparency: Acknowledging limitations demonstrates transparency and honesty in your research.
Robustness of Findings: Recognizing limitations adds nuance to your findings, making them more robust.
Future Research Directions: Addressing limitations provides a foundation for future researchers to build upon.

4. Identifying Research Limitations:

Reflect on Methodology: Consider the strengths and weaknesses of your research design, data collection methods, and analysis.
Examine Sample Characteristics: Evaluate the representativeness and size of your study sample.
Consider External Factors: Assess external factors that may impact the generalizability of your findings.

5. How to Address Limitations:

In the Methodology Section: Clearly articulate limitations in the methodology section of your research paper.
Offer Solutions: If possible, propose ways to mitigate or address identified limitations.
Future Research Suggestions: Use limitations as a springboard to suggest areas for future research.

6. Common Phrases to Express Limitations:

"This study is not without limitations."
"One limitation of our research is..."
"It is important to acknowledge the constraints of this study, including..."

7. Examples of Addressing Limitations:

Example 1 (Methodological): "While our survey provided valuable insights, the reliance on self-reported data introduces the possibility of response bias."
Example 2 (Sampling): "The small sample size of our study limits the generalizability of our findings to a broader population."
Example 3 (Resource): "Due to budget constraints, our research was limited to a single geographical location, potentially impacting the external validity."

8. Balancing Strengths and Limitations:

Emphasize Contributions: Highlight the contributions and strengths of your research alongside the limitations.
Maintain a Positive Tone: Discuss limitations objectively without undermining the significance of your study.

9. Feedback and Peer Review:

Seek Feedback: Share your research with peers or mentors to gain valuable insights.
Peer Review: Embrace the feedback received during the peer-review process to enhance the robustness of your work.

10. Continuous Reflection:

Throughout the Research Process: Continuously reflect on potential limitations during the entire research process.
Adjust as Needed: Be willing to adjust your approach as you encounter unforeseen challenges.

Conclusion:

Understanding and effectively addressing research limitations is a hallmark of rigorous and responsible scholarship. By openly acknowledging these constraints, you not only enhance the credibility of your work but also contribute to the broader academic discourse. Embrace the nuances of your research journey, navigate its limitations thoughtfully, and pave the way for future investigations.

Related Guides

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How to present limitations in research

Last updated

30 January 2024

Reviewed by

Limitations don’t invalidate or diminish your results, but it’s best to acknowledge them. This will enable you to address any questions your study failed to answer because of them.

In this guide, learn how to recognize, present, and overcome limitations in research.

What is a research limitation?

Research limitations are weaknesses in your research design or execution that may have impacted outcomes and conclusions. Uncovering limitations doesn’t necessarily indicate poor research design—it just means you encountered challenges you couldn’t have anticipated that limited your research efforts.

Does basic research have limitations?

Basic research aims to provide more information about your research topic. It requires the same standard research methodology and data collection efforts as any other research type, and it can also have limitations.

Common research limitations

Researchers encounter common limitations when embarking on a study. Limitations can occur in relation to the methods you apply or the research process you design. They could also be connected to you as the researcher.

Methodology limitations

Not having access to data or reliable information can impact the methods used to facilitate your research. A lack of data or reliability may limit the parameters of your study area and the extent of your exploration.

Your sample size may also be affected because you won’t have any direction on how big or small it should be and who or what you should include. Having too few participants won’t adequately represent the population or groups of people needed to draw meaningful conclusions.

Research process limitations

The study’s design can impose constraints on the process. For example, as you’re conducting the research, issues may arise that don’t conform to the data collection methodology you developed. You may not realize until well into the process that you should have incorporated more specific questions or comprehensive experiments to generate the data you need to have confidence in your results.

Constraints on resources can also have an impact. Being limited on participants or participation incentives may limit your sample sizes. Insufficient tools, equipment, and materials to conduct a thorough study may also be a factor.

Common researcher limitations

Here are some of the common researcher limitations you may encounter:

Time: some research areas require multi-year longitudinal approaches, but you might not be able to dedicate that much time. Imagine you want to measure how much memory a person loses as they age. This may involve conducting multiple tests on a sample of participants over 20–30 years, which may be impossible.

Bias: researchers can consciously or unconsciously apply bias to their research. Biases can contribute to relying on research sources and methodologies that will only support your beliefs about the research you’re embarking on. You might also omit relevant issues or participants from the scope of your study because of your biases.

Limited access to data : you may need to pay to access specific databases or journals that would be helpful to your research process. You might also need to gain information from certain people or organizations but have limited access to them. These cases require readjusting your process and explaining why your findings are still reliable.

Why is it important to identify limitations?

Identifying limitations adds credibility to research and provides a deeper understanding of how you arrived at your conclusions.

Constraints may have prevented you from collecting specific data or information you hoped would prove or disprove your hypothesis or provide a more comprehensive understanding of your research topic.

However, identifying the limitations contributing to your conclusions can inspire further research efforts that help gather more substantial information and data.

Where to put limitations in a research paper

A research paper is broken up into different sections that appear in the following order:

Introduction

Methodology

The discussion portion of your paper explores your findings and puts them in the context of the overall research. Either place research limitations at the beginning of the discussion section before the analysis of your findings or at the end of the section to indicate that further research needs to be pursued.

What not to include in the limitations section

Evidence that doesn’t support your hypothesis is not a limitation, so you shouldn’t include it in the limitation section. Don’t just list limitations and their degree of severity without further explanation.

How to present limitations

You’ll want to present the limitations of your study in a way that doesn’t diminish the validity of your research and leave the reader wondering if your results and conclusions have been compromised.

Include only the limitations that directly relate to and impact how you addressed your research questions. Following a specific format enables the reader to develop an understanding of the weaknesses within the context of your findings without doubting the quality and integrity of your research.

Identify the limitations specific to your study

You don’t have to identify every possible limitation that might have occurred during your research process. Only identify those that may have influenced the quality of your findings and your ability to answer your research question.

Explain study limitations in detail

This explanation should be the most significant portion of your limitation section.

Link each limitation with an interpretation and appraisal of their impact on the study. You’ll have to evaluate and explain whether the error, method, or validity issues influenced the study’s outcome and how.

Propose a direction for future studies and present alternatives

In this section, suggest how researchers can avoid the pitfalls you experienced during your research process.

If an issue with methodology was a limitation, propose alternate methods that may help with a smoother and more conclusive research project. Discuss the pros and cons of your alternate recommendation.

Describe steps taken to minimize each limitation

You probably took steps to try to address or mitigate limitations when you noticed them throughout the course of your research project. Describe these steps in the limitation section.

Limitation example

“Approaches like stem cell transplantation and vaccination in AD [Alzheimer’s disease] work on a cellular or molecular level in the laboratory. However, translation into clinical settings will remain a challenge for the next decade.”

The authors are saying that even though these methods showed promise in helping people with memory loss when conducted in the lab (in other words, using animal studies), more studies are needed. These may be controlled clinical trials, for example.

However, the short life span of stem cells outside the lab and the vaccination’s severe inflammatory side effects are limitations. Researchers won’t be able to conduct clinical trials until these issues are overcome.

How to overcome limitations in research

You’ve already started on the road to overcoming limitations in research by acknowledging that they exist. However, you need to ensure readers don’t mistake weaknesses for errors within your research design.

To do this, you’ll need to justify and explain your rationale for the methods, research design, and analysis tools you chose and how you noticed they may have presented limitations.

Your readers need to know that even when limitations presented themselves, you followed best practices and the ethical standards of your field. You didn’t violate any rules and regulations during your research process.

You’ll also want to reinforce the validity of your conclusions and results with multiple sources, methods, and perspectives. This prevents readers from assuming your findings were derived from a single or biased source.

Learning and improving starts with limitations in research

Dealing with limitations with transparency and integrity helps identify areas for future improvements and developments. It’s a learning process, providing valuable insights into how you can improve methodologies, expand sample sizes, or explore alternate approaches to further support the validity of your findings.

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How to Present the Limitations of the Study Examples

What are the limitations of a study?

The limitations of a study are the elements of methodology or study design that impact the interpretation of your research results. The limitations essentially detail any flaws or shortcomings in your study. Study limitations can exist due to constraints on research design, methodology, materials, etc., and these factors may impact the findings of your study. However, researchers are often reluctant to discuss the limitations of their study in their papers, feeling that bringing up limitations may undermine its research value in the eyes of readers and reviewers.

In spite of the impact it might have (and perhaps because of it) you should clearly acknowledge any limitations in your research paper in order to show readers—whether journal editors, other researchers, or the general public—that you are aware of these limitations and to explain how they affect the conclusions that can be drawn from the research.

In this article, we provide some guidelines for writing about research limitations, show examples of some frequently seen study limitations, and recommend techniques for presenting this information. And after you have finished drafting and have received manuscript editing for your work, you still might want to follow this up with academic editing before submitting your work to your target journal.

Why do I need to include limitations of research in my paper?

Although limitations address the potential weaknesses of a study, writing about them toward the end of your paper actually strengthens your study by identifying any problems before other researchers or reviewers find them.

Furthermore, pointing out study limitations shows that you’ve considered the impact of research weakness thoroughly and have an in-depth understanding of your research topic. Since all studies face limitations, being honest and detailing these limitations will impress researchers and reviewers more than ignoring them.

limitations of the study examples, brick wall with blue sky

Where should I put the limitations of the study in my paper?

Some limitations might be evident to researchers before the start of the study, while others might become clear while you are conducting the research. Whether these limitations are anticipated or not, and whether they are due to research design or to methodology, they should be clearly identified and discussed in the discussion section —the final section of your paper. Most journals now require you to include a discussion of potential limitations of your work, and many journals now ask you to place this “limitations section” at the very end of your article.

Some journals ask you to also discuss the strengths of your work in this section, and some allow you to freely choose where to include that information in your discussion section—make sure to always check the author instructions of your target journal before you finalize a manuscript and submit it for peer review .

Limitations of the Study Examples

There are several reasons why limitations of research might exist. The two main categories of limitations are those that result from the methodology and those that result from issues with the researcher(s).

Common Methodological Limitations of Studies

Limitations of research due to methodological problems can be addressed by clearly and directly identifying the potential problem and suggesting ways in which this could have been addressed—and SHOULD be addressed in future studies. The following are some major potential methodological issues that can impact the conclusions researchers can draw from the research.

Issues with research samples and selection

Sampling errors occur when a probability sampling method is used to select a sample, but that sample does not reflect the general population or appropriate population concerned. This results in limitations of your study known as “sample bias” or “selection bias.”

For example, if you conducted a survey to obtain your research results, your samples (participants) were asked to respond to the survey questions. However, you might have had limited ability to gain access to the appropriate type or geographic scope of participants. In this case, the people who responded to your survey questions may not truly be a random sample.

Insufficient sample size for statistical measurements

When conducting a study, it is important to have a sufficient sample size in order to draw valid conclusions. The larger the sample, the more precise your results will be. If your sample size is too small, it will be difficult to identify significant relationships in the data.

Normally, statistical tests require a larger sample size to ensure that the sample is considered representative of a population and that the statistical result can be generalized to a larger population. It is a good idea to understand how to choose an appropriate sample size before you conduct your research by using scientific calculation tools—in fact, many journals now require such estimation to be included in every manuscript that is sent out for review.

Lack of previous research studies on the topic

Citing and referencing prior research studies constitutes the basis of the literature review for your thesis or study, and these prior studies provide the theoretical foundations for the research question you are investigating. However, depending on the scope of your research topic, prior research studies that are relevant to your thesis might be limited.

When there is very little or no prior research on a specific topic, you may need to develop an entirely new research typology. In this case, discovering a limitation can be considered an important opportunity to identify literature gaps and to present the need for further development in the area of study.

Methods/instruments/techniques used to collect the data

After you complete your analysis of the research findings (in the discussion section), you might realize that the manner in which you have collected the data or the ways in which you have measured variables has limited your ability to conduct a thorough analysis of the results.

For example, you might realize that you should have addressed your survey questions from another viable perspective, or that you were not able to include an important question in the survey. In these cases, you should acknowledge the deficiency or deficiencies by stating a need for future researchers to revise their specific methods for collecting data that includes these missing elements.

Common Limitations of the Researcher(s)

Study limitations that arise from situations relating to the researcher or researchers (whether the direct fault of the individuals or not) should also be addressed and dealt with, and remedies to decrease these limitations—both hypothetically in your study, and practically in future studies—should be proposed.

Limited access to data

If your research involved surveying certain people or organizations, you might have faced the problem of having limited access to these respondents. Due to this limited access, you might need to redesign or restructure your research in a different way. In this case, explain the reasons for limited access and be sure that your finding is still reliable and valid despite this limitation.

Time constraints

Just as students have deadlines to turn in their class papers, academic researchers might also have to meet deadlines for submitting a manuscript to a journal or face other time constraints related to their research (e.g., participants are only available during a certain period; funding runs out; collaborators move to a new institution). The time available to study a research problem and to measure change over time might be constrained by such practical issues. If time constraints negatively impacted your study in any way, acknowledge this impact by mentioning a need for a future study (e.g., a longitudinal study) to answer this research problem.

Conflicts arising from cultural bias and other personal issues

Researchers might hold biased views due to their cultural backgrounds or perspectives of certain phenomena, and this can affect a study’s legitimacy. Also, it is possible that researchers will have biases toward data and results that only support their hypotheses or arguments. In order to avoid these problems, the author(s) of a study should examine whether the way the research problem was stated and the data-gathering process was carried out appropriately.

Steps for Organizing Your Study Limitations Section

When you discuss the limitations of your study, don’t simply list and describe your limitations—explain how these limitations have influenced your research findings. There might be multiple limitations in your study, but you only need to point out and explain those that directly relate to and impact how you address your research questions.

We suggest that you divide your limitations section into three steps: (1) identify the study limitations; (2) explain how they impact your study in detail; and (3) propose a direction for future studies and present alternatives. By following this sequence when discussing your study’s limitations, you will be able to clearly demonstrate your study’s weakness without undermining the quality and integrity of your research.

Step 1. Identify the limitation(s) of the study

This part should comprise around 10%-20% of your discussion of study limitations.

The first step is to identify the particular limitation(s) that affected your study. There are many possible limitations of research that can affect your study, but you don’t need to write a long review of all possible study limitations. A 200-500 word critique is an appropriate length for a research limitations section. In the beginning of this section, identify what limitations your study has faced and how important these limitations are.

You only need to identify limitations that had the greatest potential impact on: (1) the quality of your findings, and (2) your ability to answer your research question.

Step 2. Explain these study limitations in detail

This part should comprise around 60-70% of your discussion of limitations.

After identifying your research limitations, it’s time to explain the nature of the limitations and how they potentially impacted your study. For example, when you conduct quantitative research, a lack of probability sampling is an important issue that you should mention. On the other hand, when you conduct qualitative research, the inability to generalize the research findings could be an issue that deserves mention.

Explain the role these limitations played on the results and implications of the research and justify the choice you made in using this “limiting” methodology or other action in your research. Also, make sure that these limitations didn’t undermine the quality of your dissertation .

Step 3. Propose a direction for future studies and present alternatives (optional)

This part should comprise around 10-20% of your discussion of limitations.

After acknowledging the limitations of the research, you need to discuss some possible ways to overcome these limitations in future studies. One way to do this is to present alternative methodologies and ways to avoid issues with, or “fill in the gaps of” the limitations of this study you have presented. Discuss both the pros and cons of these alternatives and clearly explain why researchers should choose these approaches.

Make sure you are current on approaches used by prior studies and the impacts they have had on their findings. Cite review articles or scientific bodies that have recommended these approaches and why. This might be evidence in support of the approach you chose, or it might be the reason you consider your choices to be included as limitations. This process can act as a justification for your approach and a defense of your decision to take it while acknowledging the feasibility of other approaches.

P hrases and Tips for Introducing Your Study Limitations in the Discussion Section

The following phrases are frequently used to introduce the limitations of the study:

“There may be some possible limitations in this study.”
“The findings of this study have to be seen in light of some limitations.”
“The first is the…The second limitation concerns the…”
“The empirical results reported herein should be considered in the light of some limitations.”
“This research, however, is subject to several limitations.”
“The primary limitation to the generalization of these results is…”
“Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”
“As with the majority of studies, the design of the current study is subject to limitations.”
“There are two major limitations in this study that could be addressed in future research. First, the study focused on …. Second ….”

For more articles on research writing and the journal submissions and publication process, visit Wordvice’s Academic Resources page.

And be sure to receive professional English editing and proofreading services , including paper editing services , for your journal manuscript before submitting it to journal editors.

Wordvice Resources

Proofreading & Editing Guide

Writing the Results Section for a Research Paper

How to Write a Literature Review

Research Writing Tips: How to Draft a Powerful Discussion Section

How to Captivate Journal Readers with a Strong Introduction

Tips That Will Make Your Abstract a Success!

APA In-Text Citation Guide for Research Writing

Additional Resources

Diving Deeper into Limitations and Delimitations (PhD student)
Organizing Your Social Sciences Research Paper: Limitations of the Study (USC Library)
Research Limitations (Research Methodology)
How to Present Limitations and Alternatives (UMASS)

Article References

Pearson-Stuttard, J., Kypridemos, C., Collins, B., Mozaffarian, D., Huang, Y., Bandosz, P.,…Micha, R. (2018). Estimating the health and economic effects of the proposed US Food and Drug Administration voluntary sodium reformulation: Microsimulation cost-effectiveness analysis. PLOS. https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002551

Xu, W.L, Pedersen, N.L., Keller, L., Kalpouzos, G., Wang, H.X., Graff, C,. Fratiglioni, L. (2015). HHEX_23 AA Genotype Exacerbates Effect of Diabetes on Dementia and Alzheimer Disease: A Population-Based Longitudinal Study. PLOS. Retrieved from https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001853

Writing Limitations of Research Study — 4 Reasons Why It Is Important!

It is not unusual for researchers to come across the term limitations of research during their academic paper writing. More often this is interpreted as something terrible. However, when it comes to research study, limitations can help structure the research study better. Therefore, do not underestimate significance of limitations of research study.

Allow us to take you through the context of how to evaluate the limits of your research and conclude an impactful relevance to your results.

Table of Contents

What Are the Limitations of a Research Study?

Every research has its limit and these limitations arise due to restrictions in methodology or research design. This could impact your entire research or the research paper you wish to publish. Unfortunately, most researchers choose not to discuss their limitations of research fearing it will affect the value of their article in the eyes of readers.

However, it is very important to discuss your study limitations and show it to your target audience (other researchers, journal editors, peer reviewers etc.). It is very important that you provide an explanation of how your research limitations may affect the conclusions and opinions drawn from your research. Moreover, when as an author you state the limitations of research, it shows that you have investigated all the weaknesses of your study and have a deep understanding of the subject. Being honest could impress your readers and mark your study as a sincere effort in research.

Why and Where Should You Include the Research Limitations?

The main goal of your research is to address your research objectives. Conduct experiments, get results and explain those results, and finally justify your research question . It is best to mention the limitations of research in the discussion paragraph of your research article.

At the very beginning of this paragraph, immediately after highlighting the strengths of the research methodology, you should write down your limitations. You can discuss specific points from your research limitations as suggestions for further research in the conclusion of your thesis.

1. Common Limitations of the Researchers

Limitations that are related to the researcher must be mentioned. This will help you gain transparency with your readers. Furthermore, you could provide suggestions on decreasing these limitations in you and your future studies.

2. Limited Access to Information

Your work may involve some institutions and individuals in research, and sometimes you may have problems accessing these institutions. Therefore, you need to redesign and rewrite your work. You must explain your readers the reason for limited access.

3. Limited Time

All researchers are bound by their deadlines when it comes to completing their studies. Sometimes, time constraints can affect your research negatively. However, the best practice is to acknowledge it and mention a requirement for future study to solve the research problem in a better way.

4. Conflict over Biased Views and Personal Issues

Biased views can affect the research. In fact, researchers end up choosing only those results and data that support their main argument, keeping aside the other loose ends of the research.

Types of Limitations of Research

Before beginning your research study, know that there are certain limitations to what you are testing or possible research results. There are different types that researchers may encounter, and they all have unique characteristics, such as:

1. Research Design Limitations

Certain restrictions on your research or available procedures may affect your final results or research outputs. You may have formulated research goals and objectives too broadly. However, this can help you understand how you can narrow down the formulation of research goals and objectives, thereby increasing the focus of your study.

2. Impact Limitations

Even if your research has excellent statistics and a strong design, it can suffer from the influence of the following factors:

Presence of increasing findings as researched
Being population specific
A strong regional focus.

3. Data or statistical limitations

In some cases, it is impossible to collect sufficient data for research or very difficult to get access to the data. This could lead to incomplete conclusion to your study. Moreover, this insufficiency in data could be the outcome of your study design. The unclear, shabby research outline could produce more problems in interpreting your findings.

How to Correctly Structure Your Research Limitations?

There are strict guidelines for narrowing down research questions, wherein you could justify and explain potential weaknesses of your academic paper. You could go through these basic steps to get a well-structured clarity of research limitations:

Declare that you wish to identify your limitations of research and explain their importance,
Provide the necessary depth, explain their nature, and justify your study choices.
Write how you are suggesting that it is possible to overcome them in the future.

In this section, your readers will see that you are aware of the potential weaknesses in your business, understand them and offer effective solutions, and it will positively strengthen your article as you clarify all limitations of research to your target audience.

Know that you cannot be perfect and there is no individual without flaws. You could use the limitations of research as a great opportunity to take on a new challenge and improve the future of research. In a typical academic paper, research limitations may relate to:

1. Formulating your goals and objectives

If you formulate goals and objectives too broadly, your work will have some shortcomings. In this case, specify effective methods or ways to narrow down the formula of goals and aim to increase your level of study focus.

2. Application of your data collection methods in research

If you do not have experience in primary data collection, there is a risk that there will be flaws in the implementation of your methods. It is necessary to accept this, and learn and educate yourself to understand data collection methods.

3. Sample sizes

This depends on the nature of problem you choose. Sample size is of a greater importance in quantitative studies as opposed to qualitative ones. If your sample size is too small, statistical tests cannot identify significant relationships or connections within a given data set.

You could point out that other researchers should base the same study on a larger sample size to get more accurate results.

4. The absence of previous studies in the field you have chosen

Writing a literature review is an important step in any scientific study because it helps researchers determine the scope of current work in the chosen field. It is a major foundation for any researcher who must use them to achieve a set of specific goals or objectives.

However, if you are focused on the most current and evolving research problem or a very narrow research problem, there may be very little prior research on your topic. For example, if you chose to explore the role of Bitcoin as the currency of the future, you may not find tons of scientific papers addressing the research problem as Bitcoins are only a new phenomenon.

It is important that you learn to identify research limitations examples at each step. Whatever field you choose, feel free to add the shortcoming of your work. This is mainly because you do not have many years of experience writing scientific papers or completing complex work. Therefore, the depth and scope of your discussions may be compromised at different levels compared to academics with a lot of expertise. Include specific points from limitations of research. Use them as suggestions for the future.

Have you ever faced a challenge of writing the limitations of research study in your paper? How did you overcome it? What ways did you follow? Were they beneficial? Let us know in the comments below!

Frequently Asked Questions

Setting limitations in our study helps to clarify the outcomes drawn from our research and enhance understanding of the subject. Moreover, it shows that the author has investigated all the weaknesses in the study.

Scope is the range and limitations of a research project which are set to define the boundaries of a project. Limitations are the impacts on the overall study due to the constraints on the research design.

Limitation in research is an impact of a constraint on the research design in the overall study. They are the flaws or weaknesses in the study, which may influence the outcome of the research.

1. Limitations in research can be written as follows: Formulate your goals and objectives 2. Analyze the chosen data collection method and the sample sizes 3. Identify your limitations of research and explain their importance 4. Provide the necessary depth, explain their nature, and justify your study choices 5. Write how you are suggesting that it is possible to overcome them in the future

Excellent article ,,,it has helped me big

This is very helpful information. It has given me an insight on how to go about my study limitations.

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21 Research Limitations Examples

research limitations examples and definition, explained below

Research limitations refer to the potential weaknesses inherent in a study. All studies have limitations of some sort, meaning declaring limitations doesn’t necessarily need to be a bad thing, so long as your declaration of limitations is well thought-out and explained.

Rarely is a study perfect. Researchers have to make trade-offs when developing their studies, which are often based upon practical considerations such as time and monetary constraints, weighing the breadth of participants against the depth of insight, and choosing one methodology or another.

In research, studies can have limitations such as limited scope, researcher subjectivity, and lack of available research tools.

Acknowledging the limitations of your study should be seen as a strength. It demonstrates your willingness for transparency, humility, and submission to the scientific method and can bolster the integrity of the study. It can also inform future research direction.

Typically, scholars will explore the limitations of their study in either their methodology section, their conclusion section, or both.

Research Limitations Examples

Qualitative and quantitative research offer different perspectives and methods in exploring phenomena, each with its own strengths and limitations. So, I’ve split the limitations examples sections into qualitative and quantitative below.

Qualitative Research Limitations

Qualitative research seeks to understand phenomena in-depth and in context. It focuses on the ‘why’ and ‘how’ questions.

It’s often used to explore new or complex issues, and it provides rich, detailed insights into participants’ experiences, behaviors, and attitudes. However, these strengths also create certain limitations, as explained below.

1. Subjectivity

Qualitative research often requires the researcher to interpret subjective data. One researcher may examine a text and identify different themes or concepts as more dominant than others.

Close qualitative readings of texts are necessarily subjective – and while this may be a limitation, qualitative researchers argue this is the best way to deeply understand everything in context.

Suggested Solution and Response: To minimize subjectivity bias, you could consider cross-checking your own readings of themes and data against other scholars’ readings and interpretations. This may involve giving the raw data to a supervisor or colleague and asking them to code the data separately, then coming together to compare and contrast results.

2. Researcher Bias

The concept of researcher bias is related to, but slightly different from, subjectivity.

Researcher bias refers to the perspectives and opinions you bring with you when doing your research.

For example, a researcher who is explicitly of a certain philosophical or political persuasion may bring that persuasion to bear when interpreting data.

In many scholarly traditions, we will attempt to minimize researcher bias through the utilization of clear procedures that are set out in advance or through the use of statistical analysis tools.

However, in other traditions, such as in postmodern feminist research , declaration of bias is expected, and acknowledgment of bias is seen as a positive because, in those traditions, it is believed that bias cannot be eliminated from research, so instead, it is a matter of integrity to present it upfront.

Suggested Solution and Response: Acknowledge the potential for researcher bias and, depending on your theoretical framework , accept this, or identify procedures you have taken to seek a closer approximation to objectivity in your coding and analysis.

3. Generalizability

If you’re struggling to find a limitation to discuss in your own qualitative research study, then this one is for you: all qualitative research, of all persuasions and perspectives, cannot be generalized.

This is a core feature that sets qualitative data and quantitative data apart.

The point of qualitative data is to select case studies and similarly small corpora and dig deep through in-depth analysis and thick description of data.

Often, this will also mean that you have a non-randomized sample size.

While this is a positive – you’re going to get some really deep, contextualized, interesting insights – it also means that the findings may not be generalizable to a larger population that may not be representative of the small group of people in your study.

Suggested Solution and Response: Suggest future studies that take a quantitative approach to the question.

4. The Hawthorne Effect

The Hawthorne effect refers to the phenomenon where research participants change their ‘observed behavior’ when they’re aware that they are being observed.

This effect was first identified by Elton Mayo who conducted studies of the effects of various factors ton workers’ productivity. He noticed that no matter what he did – turning up the lights, turning down the lights, etc. – there was an increase in worker outputs compared to prior to the study taking place.

Mayo realized that the mere act of observing the workers made them work harder – his observation was what was changing behavior.

So, if you’re looking for a potential limitation to name for your observational research study , highlight the possible impact of the Hawthorne effect (and how you could reduce your footprint or visibility in order to decrease its likelihood).

Suggested Solution and Response: Highlight ways you have attempted to reduce your footprint while in the field, and guarantee anonymity to your research participants.

5. Replicability

Quantitative research has a great benefit in that the studies are replicable – a researcher can get a similar sample size, duplicate the variables, and re-test a study. But you can’t do that in qualitative research.

Qualitative research relies heavily on context – a specific case study or specific variables that make a certain instance worthy of analysis. As a result, it’s often difficult to re-enter the same setting with the same variables and repeat the study.

Furthermore, the individual researcher’s interpretation is more influential in qualitative research, meaning even if a new researcher enters an environment and makes observations, their observations may be different because subjectivity comes into play much more. This doesn’t make the research bad necessarily (great insights can be made in qualitative research), but it certainly does demonstrate a weakness of qualitative research.

6. Limited Scope

“Limited scope” is perhaps one of the most common limitations listed by researchers – and while this is often a catch-all way of saying, “well, I’m not studying that in this study”, it’s also a valid point.

No study can explore everything related to a topic. At some point, we have to make decisions about what’s included in the study and what is excluded from the study.

So, you could say that a limitation of your study is that it doesn’t look at an extra variable or concept that’s certainly worthy of study but will have to be explored in your next project because this project has a clearly and narrowly defined goal.

Suggested Solution and Response: Be clear about what’s in and out of the study when writing your research question.

7. Time Constraints

This is also a catch-all claim you can make about your research project: that you would have included more people in the study, looked at more variables, and so on. But you’ve got to submit this thing by the end of next semester! You’ve got time constraints.

And time constraints are a recognized reality in all research.

But this means you’ll need to explain how time has limited your decisions. As with “limited scope”, this may mean that you had to study a smaller group of subjects, limit the amount of time you spent in the field, and so forth.

Suggested Solution and Response: Suggest future studies that will build on your current work, possibly as a PhD project.

8. Resource Intensiveness

Qualitative research can be expensive due to the cost of transcription, the involvement of trained researchers, and potential travel for interviews or observations.

So, resource intensiveness is similar to the time constraints concept. If you don’t have the funds, you have to make decisions about which tools to use, which statistical software to employ, and how many research assistants you can dedicate to the study.

Suggested Solution and Response: Suggest future studies that will gain more funding on the back of this ‘ exploratory study ‘.

9. Coding Difficulties

Data analysis in qualitative research often involves coding, which can be subjective and complex, especially when dealing with ambiguous or contradicting data.

After naming this as a limitation in your research, it’s important to explain how you’ve attempted to address this. Some ways to ‘limit the limitation’ include:

Triangulation: Have 2 other researchers code the data as well and cross-check your results with theirs to identify outliers that may need to be re-examined, debated with the other researchers, or removed altogether.
Procedure: Use a clear coding procedure to demonstrate reliability in your coding process. I personally use the thematic network analysis method outlined in this academic article by Attride-Stirling (2001).

Suggested Solution and Response: Triangulate your coding findings with colleagues, and follow a thematic network analysis procedure.

10. Risk of Non-Responsiveness

There is always a risk in research that research participants will be unwilling or uncomfortable sharing their genuine thoughts and feelings in the study.

This is particularly true when you’re conducting research on sensitive topics, politicized topics, or topics where the participant is expressing vulnerability .

This is similar to the Hawthorne effect (aka participant bias), where participants change their behaviors in your presence; but it goes a step further, where participants actively hide their true thoughts and feelings from you.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be non-responsiveness from some participants.

11. Risk of Attrition

Attrition refers to the process of losing research participants throughout the study.

This occurs most commonly in longitudinal studies , where a researcher must return to conduct their analysis over spaced periods of time, often over a period of years.

Things happen to people over time – they move overseas, their life experiences change, they get sick, change their minds, and even die. The more time that passes, the greater the risk of attrition.

Suggested Solution and Response: One way to manage this is to try to include a wider group of people with the expectation that there will be attrition over time.

12. Difficulty in Maintaining Confidentiality and Anonymity

Given the detailed nature of qualitative data , ensuring participant anonymity can be challenging.

If you have a sensitive topic in a specific case study, even anonymizing research participants sometimes isn’t enough. People might be able to induce who you’re talking about.

Sometimes, this will mean you have to exclude some interesting data that you collected from your final report. Confidentiality and anonymity come before your findings in research ethics – and this is a necessary limiting factor.

Suggested Solution and Response: Highlight the efforts you have taken to anonymize data, and accept that confidentiality and accountability place extremely important constraints on academic research.

13. Difficulty in Finding Research Participants

A study that looks at a very specific phenomenon or even a specific set of cases within a phenomenon means that the pool of potential research participants can be very low.

Compile on top of this the fact that many people you approach may choose not to participate, and you could end up with a very small corpus of subjects to explore. This may limit your ability to make complete findings, even in a quantitative sense.

You may need to therefore limit your research question and objectives to something more realistic.

Suggested Solution and Response: Highlight that this is going to limit the study’s generalizability significantly.

14. Ethical Limitations

Ethical limitations refer to the things you cannot do based on ethical concerns identified either by yourself or your institution’s ethics review board.

This might include threats to the physical or psychological well-being of your research subjects, the potential of releasing data that could harm a person’s reputation, and so on.

Furthermore, even if your study follows all expected standards of ethics, you still, as an ethical researcher, need to allow a research participant to pull out at any point in time, after which you cannot use their data, which demonstrates an overlap between ethical constraints and participant attrition.

Suggested Solution and Response: Highlight that these ethical limitations are inevitable but important to sustain the integrity of the research.

For more on Qualitative Research, Explore my Qualitative Research Guide

Quantitative Research Limitations

Quantitative research focuses on quantifiable data and statistical, mathematical, or computational techniques. It’s often used to test hypotheses, assess relationships and causality, and generalize findings across larger populations.

Quantitative research is widely respected for its ability to provide reliable, measurable, and generalizable data (if done well!). Its structured methodology has strengths over qualitative research, such as the fact it allows for replication of the study, which underpins the validity of the research.

However, this approach is not without it limitations, explained below.

1. Over-Simplification

Quantitative research is powerful because it allows you to measure and analyze data in a systematic and standardized way. However, one of its limitations is that it can sometimes simplify complex phenomena or situations.

In other words, it might miss the subtleties or nuances of the research subject.

For example, if you’re studying why people choose a particular diet, a quantitative study might identify factors like age, income, or health status. But it might miss other aspects, such as cultural influences or personal beliefs, that can also significantly impact dietary choices.

When writing about this limitation, you can say that your quantitative approach, while providing precise measurements and comparisons, may not capture the full complexity of your subjects of study.

Suggested Solution and Response: Suggest a follow-up case study using the same research participants in order to gain additional context and depth.

2. Lack of Context

Another potential issue with quantitative research is that it often focuses on numbers and statistics at the expense of context or qualitative information.

Let’s say you’re studying the effect of classroom size on student performance. You might find that students in smaller classes generally perform better. However, this doesn’t take into account other variables, like teaching style , student motivation, or family support.

When describing this limitation, you might say, “Although our research provides important insights into the relationship between class size and student performance, it does not incorporate the impact of other potentially influential variables. Future research could benefit from a mixed-methods approach that combines quantitative analysis with qualitative insights.”

3. Applicability to Real-World Settings

Oftentimes, experimental research takes place in controlled environments to limit the influence of outside factors.

This control is great for isolation and understanding the specific phenomenon but can limit the applicability or “external validity” of the research to real-world settings.

For example, if you conduct a lab experiment to see how sleep deprivation impacts cognitive performance, the sterile, controlled lab environment might not reflect real-world conditions where people are dealing with multiple stressors.

Therefore, when explaining the limitations of your quantitative study in your methodology section, you could state:

“While our findings provide valuable information about [topic], the controlled conditions of the experiment may not accurately represent real-world scenarios where extraneous variables will exist. As such, the direct applicability of our results to broader contexts may be limited.”

Suggested Solution and Response: Suggest future studies that will engage in real-world observational research, such as ethnographic research.

4. Limited Flexibility

Once a quantitative study is underway, it can be challenging to make changes to it. This is because, unlike in grounded research, you’re putting in place your study in advance, and you can’t make changes part-way through.

Your study design, data collection methods, and analysis techniques need to be decided upon before you start collecting data.

For example, if you are conducting a survey on the impact of social media on teenage mental health, and halfway through, you realize that you should have included a question about their screen time, it’s generally too late to add it.

When discussing this limitation, you could write something like, “The structured nature of our quantitative approach allows for consistent data collection and analysis but also limits our flexibility to adapt and modify the research process in response to emerging insights and ideas.”

Suggested Solution and Response: Suggest future studies that will use mixed-methods or qualitative research methods to gain additional depth of insight.

5. Risk of Survey Error

Surveys are a common tool in quantitative research, but they carry risks of error.

There can be measurement errors (if a question is misunderstood), coverage errors (if some groups aren’t adequately represented), non-response errors (if certain people don’t respond), and sampling errors (if your sample isn’t representative of the population).

For instance, if you’re surveying college students about their study habits , but only daytime students respond because you conduct the survey during the day, your results will be skewed.

In discussing this limitation, you might say, “Despite our best efforts to develop a comprehensive survey, there remains a risk of survey error, including measurement, coverage, non-response, and sampling errors. These could potentially impact the reliability and generalizability of our findings.”

Suggested Solution and Response: Suggest future studies that will use other survey tools to compare and contrast results.

6. Limited Ability to Probe Answers

With quantitative research, you typically can’t ask follow-up questions or delve deeper into participants’ responses like you could in a qualitative interview.

For instance, imagine you are surveying 500 students about study habits in a questionnaire. A respondent might indicate that they study for two hours each night. You might want to follow up by asking them to elaborate on what those study sessions involve or how effective they feel their habits are.

However, quantitative research generally disallows this in the way a qualitative semi-structured interview could.

When discussing this limitation, you might write, “Given the structured nature of our survey, our ability to probe deeper into individual responses is limited. This means we may not fully understand the context or reasoning behind the responses, potentially limiting the depth of our findings.”

Suggested Solution and Response: Suggest future studies that engage in mixed-method or qualitative methodologies to address the issue from another angle.

7. Reliance on Instruments for Data Collection

In quantitative research, the collection of data heavily relies on instruments like questionnaires, surveys, or machines.

The limitation here is that the data you get is only as good as the instrument you’re using. If the instrument isn’t designed or calibrated well, your data can be flawed.

For instance, if you’re using a questionnaire to study customer satisfaction and the questions are vague, confusing, or biased, the responses may not accurately reflect the customers’ true feelings.

When discussing this limitation, you could say, “Our study depends on the use of questionnaires for data collection. Although we have put significant effort into designing and testing the instrument, it’s possible that inaccuracies or misunderstandings could potentially affect the validity of the data collected.”

Suggested Solution and Response: Suggest future studies that will use different instruments but examine the same variables to triangulate results.

8. Time and Resource Constraints (Specific to Quantitative Research)

Quantitative research can be time-consuming and resource-intensive, especially when dealing with large samples.

It often involves systematic sampling, rigorous design, and sometimes complex statistical analysis.

If resources and time are limited, it can restrict the scale of your research, the techniques you can employ, or the extent of your data analysis.

For example, you may want to conduct a nationwide survey on public opinion about a certain policy. However, due to limited resources, you might only be able to survey people in one city.

When writing about this limitation, you could say, “Given the scope of our research and the resources available, we are limited to conducting our survey within one city, which may not fully represent the nationwide public opinion. Hence, the generalizability of the results may be limited.”

Suggested Solution and Response: Suggest future studies that will have more funding or longer timeframes.

How to Discuss Your Research Limitations

1. in your research proposal and methodology section.

In the research proposal, which will become the methodology section of your dissertation, I would recommend taking the four following steps, in order:

Be Explicit about your Scope – If you limit the scope of your study in your research question, aims, and objectives, then you can set yourself up well later in the methodology to say that certain questions are “outside the scope of the study.” For example, you may identify the fact that the study doesn’t address a certain variable, but you can follow up by stating that the research question is specifically focused on the variable that you are examining, so this limitation would need to be looked at in future studies.
Acknowledge the Limitation – Acknowledging the limitations of your study demonstrates reflexivity and humility and can make your research more reliable and valid. It also pre-empts questions the people grading your paper may have, so instead of them down-grading you for your limitations; they will congratulate you on explaining the limitations and how you have addressed them!
Explain your Decisions – You may have chosen your approach (despite its limitations) for a very specific reason. This might be because your approach remains, on balance, the best one to answer your research question. Or, it might be because of time and monetary constraints that are outside of your control.
Highlight the Strengths of your Approach – Conclude your limitations section by strongly demonstrating that, despite limitations, you’ve worked hard to minimize the effects of the limitations and that you have chosen your specific approach and methodology because it’s also got some terrific strengths. Name the strengths.

Overall, you’ll want to acknowledge your own limitations but also explain that the limitations don’t detract from the value of your study as it stands.

2. In the Conclusion Section or Chapter

In the conclusion of your study, it is generally expected that you return to a discussion of the study’s limitations. Here, I recommend the following steps:

Acknowledge issues faced – After completing your study, you will be increasingly aware of issues you may have faced that, if you re-did the study, you may have addressed earlier in order to avoid those issues. Acknowledge these issues as limitations, and frame them as recommendations for subsequent studies.
Suggest further research – Scholarly research aims to fill gaps in the current literature and knowledge. Having established your expertise through your study, suggest lines of inquiry for future researchers. You could state that your study had certain limitations, and “future studies” can address those limitations.
Suggest a mixed methods approach – Qualitative and quantitative research each have pros and cons. So, note those ‘cons’ of your approach, then say the next study should approach the topic using the opposite methodology or could approach it using a mixed-methods approach that could achieve the benefits of quantitative studies with the nuanced insights of associated qualitative insights as part of an in-study case-study.

Overall, be clear about both your limitations and how those limitations can inform future studies.

In sum, each type of research method has its own strengths and limitations. Qualitative research excels in exploring depth, context, and complexity, while quantitative research excels in examining breadth, generalizability, and quantifiable measures. Despite their individual limitations, each method contributes unique and valuable insights, and researchers often use them together to provide a more comprehensive understanding of the phenomenon being studied.

Attride-Stirling, J. (2001). Thematic networks: an analytic tool for qualitative research. Qualitative research , 1 (3), 385-405. ( Source )

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J., & Williams, R. A. (2021). SAGE research methods foundations . London: Sage Publications.

Clark, T., Foster, L., Bryman, A., & Sloan, L. (2021). Bryman’s social research methods . Oxford: Oxford University Press.

Köhler, T., Smith, A., & Bhakoo, V. (2022). Templates in qualitative research methods: Origins, limitations, and new directions. Organizational Research Methods , 25 (2), 183-210. ( Source )

Lenger, A. (2019). The rejection of qualitative research methods in economics. Journal of Economic Issues , 53 (4), 946-965. ( Source )

Taherdoost, H. (2022). What are different research approaches? Comprehensive review of qualitative, quantitative, and mixed method research, their applications, types, and limitations. Journal of Management Science & Engineering Research , 5 (1), 53-63. ( Source )

Walliman, N. (2021). Research methods: The basics . New York: Routledge.

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How to Identify Limitations in Research

4-minute read

7th March 2022

Whether you’re a veteran researcher with years of experience under your belt or a novice to the field that’s feeling overwhelmed with where to start, you must understand that every study has its limitations. These are restrictions that arise from the study’s design, or the methodology implemented during the testing phase. Unfortunately, research limitations will always exist due to the subjective nature of testing a hypothesis. We’ve compiled some helpful information below on how to identify and accept research limitations and use them to your advantage. Essentially, we’ll show you how to make lemonade (a brilliant piece of academic work ) from the lemons you receive (the constraints your study reveals).

Research Limitations

So, let’s dive straight in, shall we? It’s always beneficial (and good practice) to disclose your research limitations . A common thought is that divulging these shortcomings will undermine the credibility and quality of your research. However, this is certainly not the case— stating the facts upfront not only reinforces your reputation as a researcher but also lets the assessor or reader know that you’re confident and transparent about the results and relevance of your study, despite these constraints.

Additionally, it creates a gap for more research opportunities, where you can analyze these limitations and determine how to incorporate or address them in a new batch of tests or create a new hypothesis altogether. Another bonus is that it helps readers to understand the optimum conditions for how to apply the results of your testing. This is a win-win, making for a far more persuasive research paper .

Now that you know why you should clarify your research limitations, let’s focus on which ones take precedence and should be disclosed. Any given research project can be vulnerable to various hindrances, so how do you identify them and single out the most significant ones to discuss? Well, that depends entirely on the nature of your study. You’ll need to comb through your research approach, methodology, testing processes, and expected results to identify the type of limitations your study may be exposed to. It’s worth noting that this understanding can only offer a broad idea of the possible restrictions you’ll face and may potentially change throughout the study.

We’ve compiled a list of the most common types of research limitations that you may encounter so you can adequately prepare for them and remain vigilant during each stage of your study.

Sample Size:

It’s critical that you choose a sample size that accurately represents the population you wish to test your theory on. If a sample is too small, the results cannot reliably be generalized across a large population.

Methodology:

The method you choose before you commence testing might seem effective in theory, but too many stumbling blocks during the testing phase can influence the accuracy and reliability of the results.

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Collection of Data:

The methods you utilize to obtain your research—surveys, emails, in-person interviews, phone calls—will directly influence the type of results your study yields.

Age of Data:

The nature of the information—and how far back it goes—affects the type of assumptions you can make. Extrapolating older data for a current hypothesis can significantly change the outcome of your testing.

Time Constraints:

Working within the deadline of when you need to submit your findings will determine the extent of your research and testing and, therefore, can heavily impact your results. Limited time frames for testing might mean not achieving the scope of results you were originally looking for.

Limited Budget:

Your study may require equipment and other resources that can become extremely costly. Budget constraints may mean you cannot acquire advanced software, programs, or travel to multiple destinations to interview participants. All of these factors can substantially influence your results.

So, now that you know how to determine your research limitations and the types you might experience, where should you document it? It’s commonly disclosed at the beginning of your discussion section , so the reader understands the shortcomings of your study before digging into the juicy bit—your findings. Alternatively, you can detail the constraints faced at the end of the discussion section to emphasize the requirements for the completion of further studies.

We hope this post will prepare you for some of the pitfalls you may encounter when conducting and documenting your research. Once you have a first draft ready, consider submitting a free sample to us for proofreading to ensure that your writing is concise and error-free and your results—despite their limitations— shine through.

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Limited by our limitations

Affiliations.

1 Medical School, University of Michigan, Ann Arbor, MI, USA. [email protected].
2 Medical School, University of Michigan, Ann Arbor, MI, USA.
PMID: 31347033
PMCID: PMC6684501
DOI: 10.1007/s40037-019-00530-x

Study limitations represent weaknesses within a research design that may influence outcomes and conclusions of the research. Researchers have an obligation to the academic community to present complete and honest limitations of a presented study. Too often, authors use generic descriptions to describe study limitations. Including redundant or irrelevant limitations is an ineffective use of the already limited word count. A meaningful presentation of study limitations should describe the potential limitation, explain the implication of the limitation, provide possible alternative approaches, and describe steps taken to mitigate the limitation. This includes placing research findings within their proper context to ensure readers do not overemphasize or minimize findings. A more complete presentation will enrich the readers' understanding of the study's limitations and support future investigation.

Keywords: Limitations; Research.

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Common pitfalls in the research process.

Jacob Shreffler ; Martin R. Huecker .

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Last Update: March 6, 2023 .

Definition/Introduction

Conducting research from planning to publication can be a very rewarding process. However, multiple preventable setbacks can occur within each stage of research. While these inefficiencies are an inevitable part of the research process, understanding common pitfalls can limit those hindrances. Many issues can present themselves throughout the research process. It has been said about academics that “the politics are so harsh because the stakes are so low.” Beyond interpersonal and political / funding concerns, prospective authors may encounter some disenchantment with the publish or perish culture. With a metric of (any) publication, the motivation to contribute meaningfully to science can be overshadowed by a compulsive drive to publish. [1] We believe in quality over quantity and highlight the importance of channeling creativity when pursuing scholarly work.

When considering embarking on a medical research project, one must begin with detailed planning. Do not underestimate the amount of time a project can take, often spanning years from conception to manuscript preparation. Will you conduct a retrospective chart review, a prospective study, or a true clinical trial with randomization and blinding? Will you systematically seek out and remove sources of bias from the study design and interpretation of results? Will you ensure the study is powered properly to justify conclusions? Will you eliminate or explain any conflicts of interest occurring among your author group? Will you fall victim to the temptation of frivolous subgroup analyses, or will you stick with the original plan? Will your study have a realistic chance at publication in a journal within your specialty, or perhaps another subfield? The study results may prove the null hypothesis, a ‘negative study,’ and therefore be difficult to publish. [2] Additionally, the intervention you find beneficial may subsequently be proven unhelpful or even dangerous, leading to prudent medical reversal. [3]

These considerations and more necessitate meticulous planning and vigilant adherence to a sound protocol. Along the way, you will encounter obstacles, pitfalls, some of which are presented in this article. But remain persistent, and your efforts will be rewarded with publication and contribution to science. This review covers common pitfalls researchers encounter and suggested strategies to avoid them.

Issues of Concern

There are five phases of research: planning phase, data collection/analysis phase, writing phase, journal submission phase, and rejections/revisions/acceptance phase.

Phase I Pitfalls: Planning a Study

The highest yield preempting of pitfalls in the research process occurs in the planning phase. This is when a researcher can set the stage for an optimal research process. Below are pitfalls that can occur during the planning phase.

Pitfall: Underestimating what committing to a research project requires

Conducting a research study and achieving publication sounds fulfilling, right?

Consider the many steps: conducting a literature search, writing an IRB proposal, planning and having research meetings, long and cumbersome data collection processes, working with statisticians or analyzing complex data, having unexpected research setbacks (e.g., subjects drop out, newly published papers on same topic, etc.), the possibility that after data collection you have no statistically (or clinically) significant findings, conducting an updated literature search, writing introduction, methods, results, and discussion sections of a paper, going through the many journal options to determine best fit while aiming for high impact factors, adhering to journal guidelines/fixing drafts, writing cover letters stating importance of the topic to respective journals, creating journal portal accounts, possibly being rejected numerous times, waiting months for journal decisions, working on numerous revisions and being informed by numerous individuals about all of the flaws in your writing and research.

Does it sound, maybe less fulfilling ?

Conducting a research project from inception to publication can be a rewarding experience. Research requires significant time. Setbacks are normal. To produce an important and sought-after research product, an individual must understand the magnitude of commitment required.

Pitfall: Choosing the wrong research pursuit/topic lacks precision

Consider an investigator interested in substance use research. The first challenge is the immense amount of research already published on this topic. Fortunately, there is still a massive amount of uncharted territory in substance use research.

It is important to understand what has been done and what is still undiscovered in your area of research. Do not simply study a topic because you find it interesting; passion is advantageous, but you should ensure that your study will contribute to some field/specialty or research in a significant way.

How does your research differ from what has been done?

How will it impact practice in a way that no previous study has?

Consider these questions when choosing a topic for research. Otherwise, you may struggle to get the work published. It can be demoralizing if you have already written your paper and realize that your paper is not going to get accepted by a reputable journal due to the presence of other papers already describing the same concepts you have.

As always, the first step is a thorough literature search.

Pitfall: Not considering research bias

A common theme noted in literature is that bias can, unfortunately, lead to failure to reproduce results, raising concerns regarding the integrity of science. [4] Bias can be considered various (inadvertent) poor strategies related to data design, analysis, and results reporting that produce spurious results and papers that perhaps should not be published. [5]

While one cannot completely eliminate bias from the research process, researchers should take steps to understand research bias in study endeavors and determine how to minimize bias during the planning phase of the study.

Pitfall: Not focusing on which variables to collect

Researchers often want to collect as much data as possible but should not build a list of variables that includes every single detail about subjects if the variables collected are unlikely to yield insight into the topic of research. The longer the data collection instrument, the higher likelihood of (human) errors (if manually data entry) and the longer duration of the data collection phase. Instead of taking time to build a database with many variables, consider cutting irrelevant variables and use that time to increase the sample size. Determine, based on your own clinical knowledge and published empirical works, which variables are most crucial.

Pitfall: Worrying about the statistics after the data has been collected

A vital part of the research process is ensuring you have a rigorous statistical approach. Involve your statistician very early in the project, preferably in the planning stages. They will have insight into the types of variables to collect and help shape the research methods. Statistical power is an important concept to consider before data collection to avoid false-negative results (Zlowodzki et al., 2006). Furthermore, other concepts, such as covariates, need to be part of the planning phase. Do not wait until after the data collection phase to give data to the statistician who cannot transform the data you have into outputs you want.

Pitfall: Not setting defined author roles

It is important to define who will be declared authors at the beginning of the research process to avoid conflict. Do most people want to be an author? Sure. Does everybody do the work worthy of authorship? No. While placing general comments in a shared document's margin may make the paper slightly better, it probably should not qualify for authorship. Review authorship criteria to determine what constitutes authorship. Clear expectations can ensure that everyone is on the same page and that everyone feels the process is fair, especially for individuals who plan to invest significant time in the project. Clear expectations for each author should occur before any writing begins, including deadlines and specific contributions. [6] [7] [6]

Pitfall: Not considering limitations of work before the paper is written

Avoid this pitfall by reviewing recent manuscripts and reading the limitations sections of these papers. Many of these limitations sections will make notions about generalizability to other populations. Some will discuss low power. Even the best papers in the top journals have many limitations. The best way to avoid or mitigate your work's limitations is to consider them during the planning phase.

How can you set up your project to limit your limitations section?

What (types of) samples should you include in your study?

Were you originally thinking of retrospective design, but it could be prospective?

What steps can you utilize to control baseline characteristics between groups?

Consider all limitations and think about how you can control these before data collection.

Phase II Pitfalls: Data Collection and Analysis

After the planning has occurred, typically after institutional review board (IRB) approval, the data collection and analysis phase can transpire. The entire team should typically stay involved throughout these phases. Below are pitfalls to avoid.

Pitfall: Not being involved in the data collection phase

It is important to be involved with the data collection phase, even if you do not personally collect data. Train the individuals who collect data to ensure all are on the same page and provide periodic oversight to ensure accuracy and quality of the data over time. [8] Do not assume the data collection phase is going smoothly – you may find yourself with a huge dataset riddled with inconsistencies or errors. Schedule periodic meetings to review data.

Pitfall: Not being involved with the statistical analysis phase

If you are not conducting the statistical analysis, do not assume that the person who is analyzing the data is 100% on the same page. Have meetings about the data, how to interpret the data, and the limitations of the data. Ask what other ways the data could be analyzed and how reviewers might negatively critique the data itself or the statistical methods.

The person conducting the analysis will not have the same familiarity with the topic. You are not going to be as familiar with the outputs. By understanding each other, you will a) have clearer, more robust methods and results in sections of the paper, b) limit critiques regarding the statistical approach/data outcomes, c) understand your research better for any presentations, discussion, or future work, and d) develop a positive collaboration for future work.

Phase III Pitfalls: The Writing Phase

The next phase is the writing phase. While this section covers pitfalls during the writing phase, for recommendations on conducting a literature search, writing, and publishing research, see StatPearls Evidence-Base Medicine Chapter: How to Write and Publish a Scientific Manuscript. [9] Below are pitfalls that can occur during the writing phase.

Pitfall: Poor or outdated references

When writing your paper, perform multiple literature searches to ensure all recent, salient references are covered—claims about recent similar work or research that frames your study if the references are outdated. Journals may even ask reviewers to comment on the presence or absence of up-to-date/suitable references. Conduct a literature search prior to data collection and stay on top of references throughout the research process as new papers become available.

Pitfall: No clearly defined purpose of the paper

Many aspects of manuscripts can get overlooked. Lack of a clear purpose statement can doom a paper to futility. Remind the readers of the goal of the project. You do not want consumers of your research to read the results section and forget what the goals/main outcomes are. The purpose statement should be located at the end of the introduction section.

Pitfall: Unclear methods making research hard to reproduce

A common concern in science is the lack of transparency in methods for reproducibility. The methods section should allow a reader to understand exactly what was done and conduct the study. Consider examining the S treng T hening the R eporting of OB servational studies in E pidemiology (STROBE) checklist for the methods (as well as other paper sections) to ensure best reporting practices for reproducibility. [10]

Pitfall: The tables and narratives are the same

Reviewers prefer you not to state findings in narratives that are in tables. Tables focus readers on the most important results and are not redundant with the written content. Make call-outs to the table in the paper's narrative sections, but do not state information found in tables.

Pitfall: Not reporting all data/outcomes

Some authors will state the main outcome of interest or have a statement such as “there were no other statistically significant findings between other groups.” Authors must report all outcomes and statistical analyses for reproducibility of the research. While this may be difficult to do with a broad approach, utilize tables and appendices to report all outcomes to show transparency and limit researcher bias.

Pitfall: Repeating results in discussion

Do not simply restate in the discussion what you already have in the results section. Utilize this section of the paper to link other references to your work and reflect on other empirical investigations' similarities or differences. Explain why your research provides an impactful contribution to the topic.

Pitfall: Making conclusions that do not align with your work

Authors sometimes note in their conclusions how the work impacts a topic due to X reason when X may be too broad a claim and the work doesn’t really support or prove that notion. Researchers should align their conclusions to their own results and highlight the significance of their findings.

Pitfall: Thinking the title is not a big deal

A strong title will help with the impact/readership of your paper. Consider keeping a short title that provides the main takeaway. Papers with more concise titles and present the study conclusion result in a bigger impact/receive more citations. [11]

Pitfall: Completing the abstract last minute

Similar to the title, do not underestimate an abstract. Journal and conference reviewers (and the general audience) may only read your abstract. The abstract must have the key results and contributions of the study and be well-written.

Phase IV Pitfalls: Submitting to a Journal

After the paper has been written, it is time to choose the journal. This phase also has numerous pitfalls. Below are pitfalls that can occur during this phase.

Pitfall: Choosing the wrong journal

Choosing the journal for your work can be overwhelming due to the number of options. Always look at the aims and scope of prospective journals. Look through the author guidelines to ensure that your manuscript adheres. This will save time. Review your reference list for any journals that appear more than once; if so, consider submitting to that journal. You do not want to submit your paper, wait two weeks, and then get a desk rejection because the editors state the paper is not aligned to the journal's aims and scope.

Additionally, researchers can aim too high and spend months (and numerous hours in journal submission portals) trying to publish a manuscript in a journal with a very large impact factor. Though admirable, if the research design and results lacking “gold standard” reporting, authors should consider a journal that is more likely to accept. Find a balance between the quality of your paper and the quality of the journal. Seek feedback from the other authors and/or senior colleagues who can provide honest feedback.

Pitfall: Poor cover letter on journal submission

Do not submit work with a flawed cover letter (errors or lack of clarity in how your work contributes to the body of literature). Spend time writing a detailed cover letter once, have it edited by someone else, and utilize that for all future projects. You can highlight the differences (e.g., the purpose of this work, our results showed) with each project. Use the cover letter to highlight the significance of the study while adhering to the disclosure guidelines (e.g., conflicts of interests, authors contributions, data releases, etc.), which will help the editorial board determine not only the suitability of the paper for the journal but also streamline the review process. [12]

Pitfall: Assuming that after the paper has been submitted to a journal, the work is done

The paper has been submitted! You think you are finished…but, unfortunately, the publishing game may still be far from over. Researchers often do not recognize the amount of time going into the submission/rejection/revisions phases. Revisions can sometimes be total overhauls, more work than writing a whole new paper. Be prepared to continue working.

Phase V Pitfalls: The Rejections, Revisions, and Acceptance Phase

Finally, perhaps the most unpredictable phase, the rejections, revisions, and acceptance phase, has unique pitfalls and other obstacles.

Pitfall: Mourning rejections too long/ “sitting on” a rejected paper

Did you get a desk to reject (i.e., the manuscript was not even sent for blind review)? That is unfortunate but common. You do not have time to sulk. Get that paper submitted somewhere else. The older the data, the less desirable your paper becomes. If the paper went in for a full review and was rejected, that may be even tougher than a desk reject because more time has elapsed. The good news is that (hopefully) you received feedback to incorporate in a revision. Do not spend too much time grieving rejections.

Pitfall: Not laying to rest rejected papers when it is indeed their time to go

Did you write a paper a couple of years ago, and you’ve submitted it to 20 different journals? The data is getting old. The topic wasn’t focused on. The sample size was small. Perhaps the project is not worth pursuing any longer. Do not give in to the sunk cost fallacy. If, however, you are proud of the work and stand by the paper, do not give up. If you believe after the numerous rejections that the topic/project is flawed, you can use this failure as a personal learning/growth opportunity. Do not repeat controllable mistakes on future projects.

Pitfall: Not addressing all of reviewer feedback

Did you get a revise and resubmit? Great news! The reviewers and editors will likely ask you to respond to each comment when you resubmit. Address all of the reviewer feedback. Take your time reading through the feedback, digest it, and re-read it. Carefully respond and decide how to revise your manuscript based on the feedback. Share the reviews and the duties of revision with coauthors. In your response to reviewers, stay professional and address each statement, even if you disagree with what is stated. If you do not respond to each statement, the reviewers often highlight the concern(s) again.

Pitfall: Thinking you know what the reviewers are going to say

Research reviewers are like a box of chocolates. You never know what you are going to get. You may be worried about a section of your paper/research approach, and the reviewers do not mention it at all in their review; instead, they criticize a section of your manuscript that you are most proud of.

In some reviews, you may get feedback like the following:

Reviewer #1

Please change lines 104-108 as I believe they are irrelevant to your study.

Reviewer #2

Please build on lines 104-108, as I believe they are the foundation of your study.

Sometimes, after multiple revisions, there are new concerns presented by the reviewers. This can be disheartening. Should some regulations restrict reviewers from bringing up new ideas/concerns during revision #7? Perhaps. Does any current rule prevent them from doing this? No.

During the review process, we must have faith that the reviewers are knowledgeable and provide fair, insightful, and constructive feedback. While the review process can be arbitrary or frustrating in some cases, peer review remains the gold standard in a scientific publication. Stay positive and persistent. Stay professional in responses to the reviewers. Remember that the review process can be very beneficial as it often leads to feedback that truly elevates your work and makes the product (and you) look better. [13]

Pitfall: Not rewarding yourself for a published paper

You did it! Celebrate your accomplishment. Reflect on the merit of your effort before you move on to other work or re-enter the cycle of IRBs, data coding, journal submissions, etc. Remember and appreciate how remarkable it is that you just contributed knowledge to the world.

Clinical Significance

Many pitfalls can occur throughout the research process. Researchers should understand these pitfalls and utilize strategies to avoid them to produce high-quality, sought-after research results that are useful for basic science and clinical practice.

Review Questions
Access free multiple choice questions on this topic.
Comment on this article.

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

Cite this Page Shreffler J, Huecker MR. Common Pitfalls In The Research Process. [Updated 2023 Mar 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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Research Limitations & Delimitations

What they are and how they’re different (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: David Phair (PhD) | September 2022

If you’re new to the world of research, you’ve probably heard the terms “ research limitations ” and “ research delimitations ” being thrown around, often quite loosely. In this post, we’ll unpack what both of these mean, how they’re similar and how they’re different – so that you can write up these sections the right way.

Overview: Limitations vs Delimitations

Are they the same?
What are research limitations
What are research delimitations
Limitations vs delimitations

First things first…

Let’s start with the most important takeaway point of this post – research limitations and research delimitations are not the same – but they are related to each other (we’ll unpack that a little later). So, if you hear someone using these two words interchangeably, be sure to share this post with them!

Research Limitations

Research limitations are, at the simplest level, the weaknesses of the study, based on factors that are often outside of your control as the researcher. These factors could include things like time , access to funding, equipment , data or participants . For example, if you weren’t able to access a random sample of participants for your study and had to adopt a convenience sampling strategy instead, that would impact the generalizability of your findings and therefore reflect a limitation of your study.

Research limitations can also emerge from the research design itself . For example, if you were undertaking a correlational study, you wouldn’t be able to infer causality (since correlation doesn’t mean certain causation). Similarly, if you utilised online surveys to collect data from your participants, you naturally wouldn’t be able to get the same degree of rich data that you would from in-person interviews .

Simply put, research limitations reflect the shortcomings of a study , based on practical (or theoretical) constraints that the researcher faced. These shortcomings limit what you can conclude from a study, but at the same time, present a foundation for future research . Importantly, all research has limitations , so there’s no need to hide anything here – as long as you discuss how the limitations might affect your findings, it’s all good.

Research Delimitations

Alright, now that we’ve unpacked the limitations, let’s move on to the delimitations .

Research delimitations are similar to limitations in that they also “ limit ” the study, but their focus is entirely different. Specifically, the delimitations of a study refer to the scope of the research aims and research questions . In other words, delimitations reflect the choices you, as the researcher, intentionally make in terms of what you will and won’t try to achieve with your study. In other words, what your research aims and research questions will and won’t include.

As we’ve spoken about many times before, it’s important to have a tight, narrow focus for your research, so that you can dive deeply into your topic, apply your energy to one specific area and develop meaningful insights. If you have an overly broad scope or unfocused topic, your research will often pull in multiple, even opposing directions, and you’ll just land up with a muddy mess of findings .

So, the delimitations section is where you’ll clearly state what your research aims and research questions will focus on – and just as importantly, what they will exclude . For example, you might investigate a widespread phenomenon, but choose to focus your study on a specific age group, ethnicity or gender. Similarly, your study may focus exclusively on one country, city or even organization. As long as the scope is well justified (in other words, it represents a novel, valuable research topic), this is perfectly acceptable – in fact, it’s essential. Remember, focus is your friend.

Need a helping hand?

Conclusion: Limitations vs Delimitations

Ok, so let’s recap.

Research limitations and research delimitations are related in that they both refer to “limits” within a study. But, they are distinctly different. Limitations reflect the shortcomings of your study, based on practical or theoretical constraints that you faced.

Contrasted to that, delimitations reflect the choices that you made in terms of the focus and scope of your research aims and research questions. If you want to learn more about research aims and questions, you can check out this video post , where we unpack those concepts in detail.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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18 Comments

Good clarification of ideas on how a researcher ought to do during Process of choice

Thank you so much for this very simple but explicit explanation on limitation and delimitation. It has so helped me to develop my masters proposal. hope to recieve more from your site as time progresses

Thank you for this explanation – very clear.

Thanks for the explanation, really got it well.

This website is really helpful for my masters proposal

Thank you very much for helping to explain these two terms

I spent almost the whole day trying to figure out the differences

when I came across your notes everything became very clear

thanks for the clearly outlined explanation on the two terms, limitation and delimitation.

Very helpful Many thanks 🙏

Excellent it resolved my conflict .

I would like you to assist me please. If in my Research, I interviewed some participants and I submitted Questionnaires to other participants to answered to the questions, in the same organization, Is this a Qualitative methodology , a Quantitative Methodology or is it a Mixture Methodology I have used in my research? Please help me

How do I cite this article in APA format

Really so great ,finally have understood it’s difference now

Getting more clear regarding Limitations and Delimitation and concepts

I really appreciate your apt and precise explanation of the two concepts namely ; Limitations and Delimitations.

This is a good sources of research information for learners.

thank you for this, very helpful to researchers

Very good explained

Great and clear explanation, after a long confusion period on the two words, i can now explain to someone with ease.

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Scientific Research and Methodology : An introduction to quantitative research and statistics

9 research design limitations.

So far, you have learnt to ask a RQ and designs studies. In this chapter , you will learn to identify:

limitations to internally valid.
limitations to externally valid.
limitations to ecologically valid.

9.1 Introduction

The type of study and the research design determine how the results of the study should be interpreted. Ideally, a study would be perfectly externally and internally valid; in practice this is very difficult to achieve. Practically every study has limitations. The results of a study should be interpreted in light of these limitations. Limitations are not necessarily problems .

Limitations generally can be discussed through three components:

Internal validity (Sect. 6.1 ): Discuss any limitations to internal validity due to the research design (such as identifying possible confounding variables). This is related to the effectiveness of the study within the sample (Sect. 9.2 ).
External validity (Sect. 5.1 ): Discuss how well the sample represents the intended population. This is related to the generalisability of the study to the intended population (Sect. 9.3 ).
Ecological validity : Discuss how well the study methods, materials and context approximate the real situation being studied. This is related to the practicality of the results to real life (Sect. 9.4 ).

Some of these limitations are imposed by the type of study. All these issues should be addressed when considering the study limitations.

Almost every study has limitations. Identifying potential limitations, and discussing the likely impact they have on the interpretation of the study results, is important and ethical.

Example 9.1 Delarue et al. ( 2019 ) discuss studies where subjects rate the taste of new food products. They note that taste-testing studies should (p. 78):

... allow generalizing the conclusions obtained with a consumer sample [...] to the general targeted population [i.e., external validity]... tests should be reliable in terms of accuracy and replicability [i.e., internal validity].

However, even with good internal and external validity, these studies often result in a 'high rate of failures of new launched products'. That is, the studies do not replicate the real world, and so lack ecological validity .

9.2 Limitations: internal validity

Internal validity refers to the extent to which a cause-and-effect relationship can be established in a study, eliminating other possible explanations (Sect. 6.1 ). A discussion of the limitations of internal validity should cover, as appropriate: possible confounding variables; the impact of the Hawthorne, observer, placebo and carry-over effects; the impact of any other design decisions.

If any of these issues are likely to compromise internal validity, the implications on the interpretation of the results should be discussed. For example, if the participants were not blinded, this should be clearly stated, and the conclusion should indicate that the individuals in the study may have behaved differently than usual.

Example 9.2 (Study limitations) Axmann et al. ( 2020 ) randomly allocated Ugandan farmers to receive, or not receive, hybrid maize seeds. One potential threat to internal validity was that farmers receiving the hybrid seeds could share their seeds with their neighbours.

Hence, the researchers contacted the $75$ farmers allocated to receive the hybrid seeds; none of the contacted farmers reported selling or giving seeds to other farmers. This extra step increased the internal validity of the study.

Maximizing internal validity in observational studies is more difficult than in experimental studies (e.g., random allocation is not possible). The internal validity of experimental studies involving people is often compromised because people must be informed that they are participating in a study.

Example 9.3 (Internal validity) In a study of the hand-hygiene practices of paramedics ( Barr et al. 2017 ) , self -reported hand-hygiene practices were very different than what was reported by peers . That is, how people self-report their behaviours may not align with how they actually behave, which influenced the internal validity of the study.

A study evaluated using a new therapy on elderly men, and listed some limitations of their study:

... the researcher was not blinded and had prior knowledge of the research aims, disease status, and intervention. As such, these could all have influenced data recording [...] The potential of reporting bias and observer bias could be reduced by implementing blinding in future studies. --- Kabata-Piżuch et al. ( 2021 ) , p. 10

9.3 Limitations: external validity

External validity refers to the ability to generalise the findings made from the sample to the entire intended population (Sect. 5.1 ). For a study to be externally valid, it must first be internally valid: if the study of not effective in the sample studied (i.e., internally valid), the results may not apply to the intended population either.

External validity refers to how well the sample is likely to represent the intended population in the RQ.

If the population is Iowans, then the study is externally valid if the sample is representative of Iowans The results do not have to apply to people in the rest of the United States (though this can be commented on, too). The intended population is Iowans .

External validity depends on how the sample was obtained. Results from random samples (Sects. 5.5 to 5.9 ) are likely to generalise to the population and be externally valid. (The analyses in this book assume all samples are simple random samples .) Furthermore, results from approximately representative samples (Sect. 5.10 ) may generalise to the population and be externally valid if those in the study are not obviously different than those not in the study.

Example 9.4 (External validity) A New Zealand study ( Gammon et al. 2012 ) identified (for well-documented reasons) a population of interest: 'women of South Asian origin living in New Zealand' (p. 21). The women in the sample were 'women of South Asian origin [...] recruited using a convenience sample method throughout Auckland' (p. 21).

The results may not generalise to the intended population ( all women of South Asian origin living in New Zealand) because all the women in the sample came from Auckland, and the sample was not a random sample from this population anyway. The study was still useful however!

Example 9.5 (Using biochar) Farrar et al. ( 2018 ) studied growing ginger using biochar on one farm at Mt Mellum, Australia. The results may only generalise to growing ginger at Mt Mellum, but since ginger is usually grown in similar types of climates and soils, the results may apply to other ginger farms also.

9.4 Limitations: ecological validity

The likely practicality of the study results in the real world should also be discussed. This is called ecological validity .

Definition 9.1 (Ecological validity) A study is ecologically valid if the study methods, materials and context closely approximate the real situation of interest.

Studies don't need to be ecologically valid to be useful; much can be learnt under special conditions, as long as the potential limitations are understood when applying the results to the real world. The ecological validity of experimental studies may be compromised because the experimental conditions are sometimes artificially controlled (for good reason).

Example 9.6 (Ecological validity) Consider a study to determine the proportion of people that buy coffee in a reusable cup. People could be asked about their behaviour. This study may not be ecologically valid, as how people act may not align with how they say they will act.

An alternative study could watch people buy coffees at various coffee shops, and record what people do in practice. This second study is more likely to be ecologically valid , as real-world behaviour is observed.

A study observed the effect of using high-mounted rear brake lights ( Kahane and Hertz 1998 ) , which are now commonplace. The American study showed that such lights reduced rear-end collisions by about $50$ %. However, after making these lights mandatory, rear-end collisions reduced by only $5$ %. Why?

9.5 Limitations: study types

Experimental studies, in general, have higher internal validity than observational studies, since more of the research design in under the control of the researchers; for example, random allocation of treatments is possible to minimise confounding.

Only well-conducted experimental studies can show cause-and-effect relationships.

However, experimental studies may suffer from poor ecological validity; for instance, laboratory experiments are often conducted under controlled temperature and humidity. Many experiments also require that people be told about being in a study (due to ethics), and so internal validity may be comprised (the Hawthorne effect).

Example 9.7 (Retrofitting) giandomenico2022systematic studied retro-fitting houses with energy-saving devices, and found large discrepancies in savings for observational studies ( $12.2$ %) and experimental studies ( $6.2$ %). The authors say that 'this finding reinforces the importance of using study designs with high internal validity to evaluate program savings' (p. 692).

9.6 Chapter summary

The limitations in a study need to be identified, and may be related to:

internal validity (effectiveness): how well the study is conducted within the sample, isolating the relationship of interest.
external validity (generalisability): how well the sample results are likely to apply to the intended population.
ecological validity (practicality): how well the results may apply to the real-world situation.

Many of the limitations are a results of the type of study.

9.7 Quick review questions

Are the following statements true or false ?

When interpreting the results of a study, the steps taken to maximize internal validity should be evaluated TRUE FALSE
If studies are not externally valid, then they are not useful. TRUE FALSE
When interpreting the results of a study, the steps taken to maximize external validity do not need to be evaluated TRUE FALSE
When interpreting the results of a study, ecological validity is about the impact of the study on the environment. TRUE FALSE

9.8 Exercises

Answers to odd-numbered exercises are available in App. E .

Exercise 9.1 A research study examined how people can save energy through lighting choices ( Gentile 2022 ) . The study states (p. 9) that the results 'are limited to the specific study and cannot be easily projected to other similar settings'.

What type of validity is being discussed here?

Exercise 9.2 Fill the blanks with the correct word: internal , external or ecological .

When interpreting the results of studies, we consider the practicality ( internal external ecological validity), the generalizability ( internal external ecological validity) and the effectiveness ( internal external ecological validity).

Exercise 9.3 A student project asked if 'the percentage of word retention higher in male students than female students?' When discussing external validity , the students stated:

We cannot say whether or not the general public have better or worse word retention compared to the students that we will be studying.

Why is the statement not relevant in a discussion of external validity?

Exercise 9.4 Yeh et al. ( 2018 ) conducted an experimental study to 'determine if using a parachute prevents death or major traumatic injury when jumping from an aircraft'.

The researchers randomised $23$ volunteers into one of two groups: wearing a parachute, or wearing an empty backpack. The response variable was a measurement of death or major traumatic injury upon landing. From the study, death or major injury was the same in both groups (0% for each group). However, the study used 'small stationary aircraft on the ground, suggesting cautious extrapolation to high altitude jumps' (p. 1).

Comment on the internal, external and ecological validity.

Exercise 9.5 A study examined how well hospital patients sleep at night ( Delaney et al. 2018 ) . The researchers state that 'convenience sampling was used to recruit patients' (p. 2). Later, the researchers state (p. 7):

... while most healthy individuals sleep primarily or exclusively at night, it is important to consider that patients requiring hospitalization will likely require some daytime nap periods. This study looks at sleep only in the night-time period $22$ : $00$ -- $07$ : $00$ h, without the context of daytime sleep considered.

Discuss these issues using the language introduced in this chapter.

Exercise 9.6 Botelho et al. ( 2019 ) examined the food choices made when subjects were asked to shop for ingredients to make a last-minute meal. Half were told to prepare a 'healthy meal', and the other half told just to prepare a 'meal'. The authors stated (p. 436):

Another limitation is that results report findings from a simulated purchase. As participants did not have to pay for their selection, actual choices could be different. Participants may also have not behaved in their usual manner since they were taking part in a research study, a situation known as the Hawthorne effect.

What type of limitation is being discussed?

Exercise 9.7 Johnson et al. ( 2018 ) studied the use of over-the-counter menthol cough-drops in people with a cough. One conclusion from the observational study of $548$ people was that, taking 'too many cough drops [...] may actually make coughs more severe', as one author explained in an interview about the study Critique this statement.

Research Limitations

It is for sure that your research will have some limitations and it is normal. However, it is critically important for you to be striving to minimize the range of scope of limitations throughout the research process. Also, you need to provide the acknowledgement of your research limitations in conclusions chapter honestly.

It is always better to identify and acknowledge shortcomings of your work, rather than to leave them pointed out to your by your dissertation assessor. While discussing your research limitations, don’t just provide the list and description of shortcomings of your work. It is also important for you to explain how these limitations have impacted your research findings.

Your research may have multiple limitations, but you need to discuss only those limitations that directly relate to your research problems. For example, if conducting a meta-analysis of the secondary data has not been stated as your research objective, no need to mention it as your research limitation.

Research limitations in a typical dissertation may relate to the following points:

1. Formulation of research aims and objectives . You might have formulated research aims and objectives too broadly. You can specify in which ways the formulation of research aims and objectives could be narrowed so that the level of focus of the study could be increased.

2. Implementation of data collection method . Because you do not have an extensive experience in primary data collection (otherwise you would not be reading this book), there is a great chance that the nature of implementation of data collection method is flawed.

3. Sample size. Sample size depends on the nature of the research problem. If sample size is too small, statistical tests would not be able to identify significant relationships within data set. You can state that basing your study in larger sample size could have generated more accurate results. The importance of sample size is greater in quantitative studies compared to qualitative studies.

4. Lack of previous studies in the research area . Literature review is an important part of any research, because it helps to identify the scope of works that have been done so far in research area. Literature review findings are used as the foundation for the researcher to be built upon to achieve her research objectives.

However, there may be little, if any, prior research on your topic if you have focused on the most contemporary and evolving research problem or too narrow research problem. For example, if you have chosen to explore the role of Bitcoins as the future currency, you may not be able to find tons of scholarly paper addressing the research problem, because Bitcoins are only a recent phenomenon.

5. Scope of discussions . You can include this point as a limitation of your research regardless of the choice of the research area. Because (most likely) you don’t have many years of experience of conducing researches and producing academic papers of such a large size individually, the scope and depth of discussions in your paper is compromised in many levels compared to the works of experienced scholars.

You can discuss certain points from your research limitations as the suggestion for further research at conclusions chapter of your dissertation.

My e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection to the research area to submitting the completed version of the work within the deadline. John Dudovskiy

Current progress and limitations of research regarding the therapeutic use of adipose-derived stem cells: literature review

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Published: 17 April 2024

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Maksym Skrypnyk ORCID: orcid.org/0000-0002-9552-4098 1

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Adipose tissue has recently become one of the most promising and predominant sources of mesenchymal stem cells owing to its high accessibility, culturing properties, regenerative potential, and relatively fewer ethical considerations. From the time of the adipose-derived stem cells (ADSCs) discovery, many beneficial properties have been found, including their regenerative, anti-inflammatory, immunomodulatory, and antimicrobial effects. The number of publications and clinical trials using ADSCs has increased significantly worldwide, attesting to the promising nature of the therapeutic properties of ADSCs.

Main body of the abstract

In clinical studies, ADSCs are mainly used to treat wounds, multiple sclerosis, soft tissue trauma, aging, diabetes, Parkinson’s disease, bone and cartilage regeneration, strokes, and spinal cord injuries. Few and insignificant adverse effects after ADSC treatment have been documented, suggesting their relative safety for clinical use. Despite significant progress in ADSC-related studies, several issues are yet to be addressed, including a lack of standardization of ADSC-associated protocols and the methods used to obtain them, inconsistent dosages, small numbers of patients in each treatment group, and variable graft purity. This severely complicates our ability to compare these studies, making the results even of similar studies controversial.

Short conclusion

This review described the current stage of ADSCs-based treatment outcomes and their limitations, associated with standardization of ADSCs.

Adipose-Derived Stem Cells: In Musculoskeletal Disorders

Adipose-Derived Stem Cells as a Novel Tool for Future Regenerative Medicine

Human Adipose-Derived Stem Cells (ASC): Their Efficacy in Clinical Applications

Avoid common mistakes on your manuscript.

1 Background

Regenerative medicine is a relatively new branch of science that aims to replace aged, damaged, and disease- or trauma-affected tissues and organs, and to stimulate organismal regenerative potential.

Stem cell therapy involves several mechanisms of action. One is direct replacement of damaged cells and tissues [ 1 ]. Another is a paracrine mechanism that involves modulation of the microenvironment, activation of the native immunity, anti-inflammatory effect and prevention of fibrosis development, pain relief through the secretion of cytokines, regulation of cell death, and immunomodulatory effect [ 2 , 3 ]. Stem cell therapy is considered one of the most promising and highly effective treatment methods for several inflammatory diseases, infectious diseases, non-communicable diseases, cancer, age-related pathologies, pediatric diseases and rejuvenation [ 4 , 5 , 6 ].

Despite this, stem cell therapy is still not widespread and is even forbidden in some countries. Based on available data, no more than 15 allogeneic mesenchymal stem cell (MSC) products have been approved worldwide [ 7 ]. Implementation rates of stem-cell-based therapeutic products remain low, but they have been gradually increasing; as of 17 August 2022, twenty-four cellular and gene therapy products have been licensed by the Office of Tissues and Advanced Therapies (USA) [ 8 ]. Medical tourism to seek stem-cell-based therapies has increased significantly despite the small number of clinical studies and poor evidence base for such therapies [ 7 , 9 ].

Initially MSCs were first found in bone marrow in 1976. It has been shown that MSCs, which are multipotent, can differentiate into mesenchymal, endodermal, and ectodermal cell lines [ 10 ]. Bone marrow is the gold-standard source of MSCs [ 11 ]. The most common harvesting site for bone marrow is the iliac crest, followed by the proximal femur [ 12 ]. However, bone marrow aspiration has significant drawbacks due to its high invasiveness and low MSCs yield [ 13 ]. Even MSCs harvested from different bones of the same individual differ in terms of their regenerative potential and cell concentration, and their effects vary between in-vivo and in-vitro settings [ 14 ]. Bone marrow biopsy is poorly tolerated by patients because of post-procedure pain, and most patients experience anxiety before and during the procedure, even in the case of experienced bone marrow donors [ 15 , 16 ]. Owing to these limitations, alternative MSC donor sites and new approaches are in high demand. Connective tissue and stromal components of inner organs are graft-rich sources for MSCs isolation. One of these is adipose tissue, which is in abundance in a human body. The high proliferation and differentiation capacity of adipose-derived stem cells (ADSCs) and their more accessible donor sites make them a more promising and less invasive alternative to bone-marrow MSCs (BM-MSCs) for stem-cell-based therapies [ 17 ]. ADSCs and BM-MSCs have similar characteristics in terms of their morphology, properties, and receptors[ 18 ]. There are also other sources of adult tissue-derived MSC such as peripheral blood, endometrium, tooth pulp, and breast milk [ 19 ]. Umbilical cord, cord blood, placenta and amniotic fluid are the neonatal sources of stem cells [ 20 ]. Adult and neonatal stem cells have various clinical applications and their own advantages and disadvantages. Neonatal stem cells have higher proliferative capacity, potential growth by multi-layering due to the absence of contact inhibition, no senescence over passaging and lower immunogenicity, and higher immunosuppressive capacity [ 20 ]. Despite possessing better immunological properties, neonatal stem cells have several disadvantages that limit their clinical application such as low cell amount in a single cord blood unit, single time collection, high storage cost etc. [ 21 ].

In most circumstances, only the allogeneic application of neonatal stem cells is possible, while BM-MSCs, ADSCs and peripheral blood stem cells can also be used in autologous settings, which significantly facilitates ethical issues, prevents infections from spreading, and provides a limitless source of cells [ 22 ]. Moreover, BM-MSCs are not immune-privileged and have immunogenic potential in allogeneic settings [ 23 ]. Two strategies exist for prolonging their persistence and improving the efficacy of stem cell therapy: modifying the host immune system response or modifying the antigen properties of MSCs [ 24 ].

The range of application of stem cells-based treatment in clinical medicine expands every year, especially adipose tissue as one of the favorable sources of stem cells found a broad application in tissues engineering [ 25 , 26 ]. This review aims to summarize current understandings of ADSC biology, to discuss the latest ADSC-based experimental studies and clinical trials, and to highlight the current advantages and limitations of using ADSCs in medicine.

A systematic search in the PubMed and Scopus database was conducted on 12 October 2023 for all studies including ADSCs, BM-MSCs and MSCs. Original articles, review articles, meta-analysis, clinical cases and case series written in English were selected for review. The search strategy included usage of the following terms: “adipose-derived stem cells”, “fat-derived stem cells”, “bone marrow-derived stem cells”, “mesenchymal stem cells” and their synonyms. Retrieved articles, relative to the review topic, were stored in a database and duplicates were removed.

2 Main text

2.1 status of research regarding regenerative medicine using adscs.

ADSCs were first retrieved from lipoaspirates by Zuk et al. in 2001 [ 27 ]. While BM-MSCs were historically discovered earlier than ADSCs, their clinical application is sometimes limited. This is why an alternative source of MSCs is required. One of them is ADSCs that have been intensively studied worldwide owing to their relative ease of isolation, few ethical considerations, non-invasive harvesting procedure, good culturing properties, and promising results in in-vitro and in-vivo research.

Medical stem cell therapy is flourishing worldwide; however, patients sometimes have unsubstantiated expectations regarding stem cell therapy. Sometimes, stem cell treatment is provided without proper indications and has life-threatening consequences [ 28 ]. The high cost of treatment, low quality, long waiting times, jurisdictional legal restrictions, inability to participate in clinical trials, and lack of access to unapproved treatments lead patients to engage in stem cell tourism. The leaders in international MSC tourism are the USA, China, India, Thailand, and Mexico [ 29 ].

In Japan, in addition to laws governing clinical trials conducted under the International Conference on Harmonisation – Good Clinical Practice and the requirement for the approval of regenerative medical products ( Pharmaceutical and Medical Device Act ), the Act on the Safety of Regenerative Medicine governs the implementation of regenerative medicine in clinical trials or as a treatment. Due to the Regenerative Medicine Act all the procedures were classified into risk categories (high, intermediate and low risk, which are Class I, II and III respectively), among which treatment and research using ADSCs are being conducted in various clinical departments, including the orthopedic and dental fields [ 30 ].

Around the world, stem cell therapies, including those using ADSCs, are offered in clinical practice, with the main clinical indications being multiple sclerosis, cellular therapy of cornea injuries, chronic pulmonary disease, rejuvenation, Parkinson's disease, bone and soft tissues augmentation and regeneration which were destroyed due periodontitis, stroke therapy, severe spinal cord injury, cerebral palsy, chronic wound healing, autism, amyotrophic latent arteriosclerosis, Alzheimer's disease, and inflammatory joints disease [ 31 ].

A search of “adipose-derived stem cells” on www.clinicaltrials.gov found that more than 394 clinical trials using ADSCs have been conducted worldwide, 132 of which have already been completed. In clinical trials, ADSCs have been used for face rejuvenation, keloid treatment, reconstructive surgery, alopecia treatment, arthritis therapy, periodontal therapy, diabetic wound healing, and many other purposes.

2.2 Biology of adipose tissue

Adipose tissue is a connective tissue with special properties. Approximately 20–25% percent of a healthy individual’s weight is adipose tissue. Based on morphological differences, adipocytes were distributed into white, brown and bright (beige) adipocytes. Depending on its location, adipose tissue is classified into subcutaneous (located under the skin) or visceral (around inner organs) fat. White adipocytes are found in white adipose tissue (WAT), and cell shape varies from spherical to oval or polyhedral. Almost the entire cell volume is occupied by a unilocular lipid droplet which occupied the central part of an adipocyte and flattens to the periphery nucleus. The adipocyte`s lipid droplets are lost on a histological section during the traditional way of tissues preparation, which gives WAT a thin polygonal mesh appearance [ 32 , 33 ]. Visceral adipose tissue (VAT) presented as abdominal viscera, mesenterium and omentum, has completely different qualities compared to WAT. Adipocytes type, their secretome, endocrine regulation, proliferation rate, lipolytic activity, sensitivity to insulin and other hormones differ between subcutaneous WAT and visceral fat. Macrophages are more prevalent in VAT compared with subcutaneous WAT [ 34 ].

Brown adipose tissue is common in newborns and located in the neck, back and shoulder areas. With maturation, brown fat scatters around the body. In adults it is located around the neck and inner organs such as the kidneys, adrenal glands, aorta and mediastinum. Brown adipocytes are much smaller compared to white and beige adipocytes, and their lipids are distributed into numerous lipid droplets, with their nucleus located at the cell center. These cells are abundant in mitochondria, with a brown appearance. The major function of these cells is to produce heat. There are two types of brown adipocytes: high- and low-thermogenic adipocytes [ 35 ].

Beige adipocytes are a recently discovered type of brown adipocyte located in subcutaneous fat depots, such as the inguinal and anterior subcutaneous WAT; however, a small number can also be found in VAT [ 36 ]. Beige adipocytes have a multilocular morphology. Properties, cultural and functional differences of white, brown and beige adipocytes summarized in Table 1 .

Similar to every connective tissue, adipose tissue presented as cells surrounded by an extracellular matrix. Cells percentage in adipose tissue is significantly prevail under the extracellular matrix component. Adipocyte is a minimal structural and functional unit of adipose tissue. Besides adipocytes, adipose tissue also consists of preadipocytes, fibroblasts, capillary endothelial cells, macrophages, and stem cells, all of which form the stromal vascular fraction (SVF) that supports, supplies, and protect adipocytes [ 36 ]. Adipose tissue has a good blood supply and is innervated by unmyelinated nerves [ 37 ].

In mammals, adipose tissue has the following important functions: energy storage, hormone secretion, metabolism, protection, and thermogenesis. In recent years, adipose tissue has been considered as a powerful endocrine organ because it produces several hormones such as estrogen, leptin, adiponectin, resistin, and biologically active substances such as TNF, IL-6, IL-1, CCL2, MCP1, PAI-1, and complement factors [ 38 , 39 ].

SVF is one of the adipose tissue components that is a mixture of cells contained within adipocytes that is traditionally isolated by enzymatic digestion. After adipocytes extraction, connective tissue and blood from lipoaspirate, come the SVF, a mix including MSC, endothelial precursor, T-reg, adipose tissue macrophages, smooth muscle cells, pericytes and preadipocytes [ 40 , 41 ].

2.3 Adipose tissue as a source of MSC

WAT is a huge source of MSCs with superior culturing properties. In humans that WAT has an abundance of CD-34+ -cells, immunohistochemical analysis has confirmed that CD-34+ cells are evenly distributed among white adipocytes [ 10 ]. It has been shown that about 5 × 10 5 stem cells can be isolated from a few milligrams of adipose tissue with the possibility of continuously culturing in vitro for up to one month without cell passaging [ 42 ]. Adipose tissue is a prospective source of MSCs owing to variable donor sites, the large quantity of biological sources from deceased donors, and routine deceased-donor workups [ 43 , 44 ]. Studies have shown that WAT harvested from the abdomen of deceased, research-consenting donors indicated that the total nucleated cell count was even higher than that in living donors, and the morphology and functional properties (growth potential, gene expression level, and differentiation ability) of the cell culture were similar [ 43 , 44 ]. However, changes in the properties and biology of adipose tissue in obese individuals are a general health condition [ 39 , 45 , 46 , 47 ]. Isolated ADSCs from VAT and subcutaneous WAT had no differences in morphology and had the same expression of CD antigens. However, the growth rate of subcutaneous WAT ADSCs is 1.75 faster than ADSCs isolated from VAT also ADSCs were different in terms of angiogenic and inflammatory cytokines level. ADSCs from subcutaneous WAT have significantly lower concentrations of chitinase 3-like 1, IL-1β, EGF, MCP-1, Cystatin C, IL-6, IL-8, Pentraxin 3, TGF-β, plasminogen activator urokinase receptor and TNF-α [ 48 ].

There are three main criteria for ADSCs. Firstly, MSCs must have adherent growth; trilineage mesenchymal differentiation (adipocytes, osteoblasts, and chondroblasts). Secondly, ADSCs must express surface specific antigens such as expressing MSCs markers like CD44, CD105, CD90, and CD73, which are progenitors in subcutaneous WAT, and their phenotype is similar to BM-MSCs. Thirdly, ADSCs do not express the HLA-DR protein or MHC Class I molecules, which enable the possibility of allogeneic transplantation [ 49 ].

Some scientists considered that ADSCs to be immune-privileged cells [ 41 ]. The concept of immune privilege means that some biological grafts can survive in the recipient’s body for a certain time without triggering a graft-versus-host response or large-scale destructive inflammation in the place of application [ 50 ]. However, other studies have shown that MSCs are not completely immune-privileged, due to the triggering of both humoral and cellular immune responses in vivo, which depends on the microenvironment [ 24 ]. For example, the second transplantation of allogeneic MSCs from the same donor in mice resulted in accelerated rejection of cells, which attests to the formation of T-cell memory [ 51 ]. It was reported that ADSCs have superior immunomodulatory action because of the less MHC class II expression that makes them a prospective graft material for allogenic treatment [ 52 ]. Allogeneic ADSCs have immunological potential and can trigger graft rejection and inflammation in the recipient’s body. Introducing the human cytomegalovirus US2/US3 gene into ADSCs reduced ADSC immunogenicity and graft rejection by decreasing MHC I protein expression [ 53 ]. This method is promising for obtaining the same effect after transplantation of allogeneic ADSCs as autogenetic ADSCs [ 53 ]. Was reported that immunomodulatory effect related to the regenerative capacity has been increasing [ 52 ]. Moreover, it was shown that MSCs are able to produce molecules which have antimicrobial and analgetic properties, making them a prospective therapeutic agent against cytokine storm- infections [ 54 , 55 ].

Several studies also indicate promising clinical results with brown adipocyte transplantation for the treatment of diabetes and obesity [ 56 ]. In experimental research, brown adipocyte transplantation improved the regulation of adipose tissue and glucose homeostasis as well as insulin resistance [ 57 ]. However, the specific mechanisms behind these effects have not yet been discovered [ 35 ].

2.4 Mechanism of ADSCs action

ADSCs therapy is based on direct replacement of damaged cells with differentiated ADSCs or modification of local paracrine signaling by extracellular vesicles (see Fig. 1 ). Studies report that under different conditions in vitro, ADSCs can differentiate into ectodermal, mesodermal, and endodermal progenitors [ 11 , 17 , 58 , 59 ]. However, only several of these studies reported a successful result in in-vivo studies or clinical trials. Differentiation of ADSCs in vivo is challenging due to poor cell survival, mostly because of the transplantation of cells into organs with a hypoxic environment. However, compared with mature adipocytes, ADSCs have higher survival rates because of less sensitivity to ischemia and secretion of angiogenic factors that stimulate local angiogenesis [ 60 ].

Possible mechanism of ADSCs action via direct cell replacement and paracrine signaling

Studies reported the successful usage of ADSCs in endometrial injury treatment. ADSCs underwent differentiation into mature endometrial epithelial cells, which resulted in endometrial structure and function regeneration [ 61 ]. However, most of studies are limited to the in vitro demonstration of ADSCs differentiation such as differentiation of ADSCs into insulin-producing cells, cells with hepatocytic function, osteocytes, adipocytes etc. [ 58 , 60 , 62 , 63 ]. Nowadays, clinical translation of ADSC-based therapy for a direct cell’s replacement is difficult since most of the mechanisms for stem cells differentiation in the in vivo setting remains unclear. Such treatment might possibly result in the initial stages of cancer development and other adverse results [ 64 ]. ADSCs under inflammation regulate the inflammatory stimuli, triggering the synthesis of pro-angiogenic factors such as VEGF-A, hepatocyte growth factors, and IGF-1 as well as that of hematopoietic cytokines such as macrophage-colony stimulating factor, granulocyte-colony stimulating factor, IL-6, TNF-α [ 65 ].

Another more promising implication of ADSCs is via regulation of local tissue homeostasis. ADSCs possess unique paracrine characteristics. It is realized through extracellular vesicles (EVs) which contain products of cell secretion and transport it to the target cells to regulate cell function and change their phenotype via cell signaling. EVs are secreted by many different cell types, including ADSCs. They contain microRNA, mRNA, lipids, and proteins, and are classified as microvesicles (50–1000 nm in size) and exosomes (30–100 nm)[ 66 , 67 ].

Recently, several promising results of treatment using isolated from ADSCs exosomes were shown. Exosomes of ADSCs contain numerous growths regulating cytokines that enhance recovery of damaged tissue and growth factors that mediate tissue regeneration. These growth factors are: basic fibroblast growth factor, VEGF-A, insulin-like growth factor 1, hepatocyte growth factors, and transforming growth factor, brain-derived neurotrophic factor, nerve growth factor, and glial-derived neurotrophic factor, matrix metalloproteinase- (MMP-) 3 and MMP-9 [ 68 , 69 ].

ADSCs exosomes treatment showed promising results in therapy of neurological diseases, liver fibrosis, myocardial ischemic injuries, endocrine diseases, bone and skin regeneration. Isolated ADSCs exosomes were used for the treatment of ischemic brain injury. They reduced brain ischemia caused by the microglial polarization, which was caused by the delivery of microRNA to inhibit the expression of signal transducers and activators of transcription 1 and phosphatase and tensin homolog deleted on chromosome ten (PTEN) [ 70 ]. Metastasis-associated lung adenocarcinoma transcript 1 was identified as one of the ADSCs exosomes component that contributes to increased neuronal survival and proliferation in traumatic brain injury or other neurodegenerative diseases [ 71 , 72 ]. Mouse ADSC EVs reduced apoptosis of motor neurons of in vitro amyotrophic lateral sclerosis model under the condition of oxidative stress alteration [ 73 ].

Further, exosomes of ADSCs decrease hepatic fibrosis development through the suppression of autophagy, PI3K/AKT/mTOR,,TGF-β/smad, Wnt/β-catenin, LPS/TLR4, EMT/ERK1, PPAR-γ, NF-κB signaling pathways and by the changing of lipid metabolism through regulation of choline metabolism [ 74 , 75 ]. ADSCs exosomes also suppress the proliferation rate of stellate cells through stimulation of apoptosis and arrest of G1 phase of the cell cycle, and through the inhibition of profibrogenic proteins and epithelio-mesenchymal transition [ 76 ]. ADSCs exosome therapy reduced liver damage by downregulation of collagen I, vimentin, α-SMA and fibronectin in liver via selectively transfer of miR-181-5p to affected hepatocytes [ 77 ].

Exosomes isolated from ADSCs are used for the therapy of diabetes mellitus associated erectile dysfunction. They enhance the secretion of the endothelial markers and downregulate caspase-3 after the operation [ 78 ]. ADSCs exosomes activate functional recovery and activate endogenous repair mechanisms of corpus cavernosum via micro RNA 126, 130a and 132 that provides angiogenesis and restore erectile function, and inhibit fibrosis in corpus cavernosum by antifibrotic properties of micro RNA-let7b and c [ 79 ]. Zhao et al. showed that ADSCs exosomes-based treatment induces endometrial regeneration and fertility restoration by collagen remodeling and enhancement of integrin-β3, LIF, and VEGF expression [ 74 ]. EV isolated from human ADSCs increase wound healing and restore the function and prevent scar formation via activation of PI3K/AKT pathway in sebocytes on a murine model [ 80 ].

However, ADSCs and their exosomes have very variable biological properties and cytokine content, even if they were harvested from the SCAT of the same donor but from a different anatomical location. Thus, the thigh fat had a significantly higher cytokines profile except for IL-1β and IL-6, compared with abdominal and chin sites [ 81 ]. Nowadays, standardised issue of ADSC-based therapy, that determine their mechanism of action, is one of a several major limitations of its clinical translation.

2.5 Factors impacting the clinical effectiveness of ADSCs treatment

The result of stem cell treatment depends on the general health of the cell donor. Thus, in patients with diabetes mellitus, type II ADSCs exhibit impaired viability and proliferation rate, mitochondrial dysfunction, senescence phenotype, impaired glucose homeostasis, and insulin sensitivity. Significantly low secretion of VEGF, adiponectin, and CXCL-12, in the background of hypo concentration of leptin, were observed among type-II ADSC samples [ 82 ]. General systemic diseases lead to disturbances in the function and morphology of ADSCs and reduce their therapeutic properties.

Co-transplantation of ADSCs and platelet-rich plasma (PRP) resulted in significantly increased alveolar bone and gingiva regeneration [ 83 , 84 ]. Moreover, PRP activates ADSCs by increasing cytokines and growth factors production, and a fibrin network can be used as a scaffold for the stem cells and to create a conducive microenvironment that increases stemness and prolongs cellular survival rate and duration [ 83 , 85 , 86 ]. Mechanical tension significantly enhances osteoblastic ADSCs differentiation; however, the mitotic activity of ADSCs is not affected by mechanical tension [ 85 ]. Li et al. showed that pretreatment of freshly isolated ADSCs with thymosin beta 4 (Tβ) upregulates the expression of genes associated with cell division, decrease cells doubling time and apoptosis [ 87 ].

In reconstructive surgery, transplantation of ADSCs alone for regenerative purposes is not as effective as co-transplantation with a composition of different cells to create a favorable environment for revascularization, preventing graft resorption and necrosis. In particular, transplantation of ADSCs, adipocytes, and endothelial cells implanted into the extracellular matrix has shown a higher cellular survival rate and volume maintenance when compared to non-prevascularized control grafts [ 86 ].

The injection of ADSCs along with intraoral administration of sildenafil citrate, which enhances blood supply and NO synthesis in animal models, significantly improves the healing rate after colon anastomosis and better reduces inflammation when compared with ADSCs alone [ 88 ]. For promoting hair growth ADSCs pretreated with bee venom is reported to increase the release of fibroblast -1 and -6, endothelial and platelet growth factors and enhancement of cells migration [ 89 ].

The actions of ADSCs are determined by their environment. Human ADSCs transferred to non-inflamed mouse lungs resulted in development of mild low-grade inflammation, which can be associated with apoptotic graft or heterotransplant clearance. T-cells that produce IFNγ can activate the immune response to efferocytosis, thus altering lung homeostasis [ 90 ]. The combination of Shilajit (a herbomineral natural substance) and an alginate hydrogel environment induced osteogenic differentiation of ADSCs into osteoblasts in a short period of time [ 91 ]. Thus, a proper microenvironment can significantly enhance the outcome of ADSCs clinical applications. There are still many concerns about safety of ADSCs therapy, thus, EV from ADSCs showed suppression of breast cancer tumor growth meanwhile the components of cell growth medium had an opposite effect of a tumor [ 92 ].

2.6 Standardization of ADSCs

The translation of novel findings in stem cell therapy to clinical practice has been discouragingly limited and ambiguous, with the effectiveness of some forms of stem cell therapies remaining poorly supported by evidence. The main problem that limits the clinical application of stem cells, in addition to many other biological medical products, is poor standardization and a lack of comprehensive guidelines [ 93 ]. Standardization of biological grafts is necessary because it offers an opportunity to compare research outcomes, which leads to the optimization of ADSC-based treatment.

It is impossible to effectively translate the results of basic research to clinical settings due to differences in cell origins, cultivation conditions, obtainment methods, and the number of cell passages. Tragoonlugkana et al. showed that cell culture plates coated with platelet lysate significantly increased properties of ADSCs such as adhesion, proliferation speed and growth as well as the cells’ viability [ 94 ]. Thus, the same method of adipose tissue harvesting, but used by different commercial systems, influences the cellular content and cytokine secretion of ADSCs [ 95 ]. Distinctive changes in gene expression have been observed after a 48-h ADSCs cultivation period. Regulatory genes are involved in cell morphogenesis and metabolism, cell-to-substrate adhesion, glycoprotein metabolic processes, and regulation of fiber molecular structure organization. Downregulated genes were those involved in cell proliferation, differentiation, and transformation [ 96 ].

Cultural, biological, and functional properties of ADSCs depend on the anatomical location of fat, age, gender, and BMI of patients [ 97 , 98 , 99 ]. It is not yet clear whether isolated cells are actually ADSCs or what types of cells they are able to generate. Researchers agree that not all MSCs have identical characteristics, which can depend on the patient’s age, donor site, isolation technique, and growth [ 100 ]. Close attention should also be paid to the origin of the allogeneic graft, since several studies have underlined that donor age, sex, tissue source, and method of isolation have an effect on cellular and molecular variability [ 101 , 102 ]. Another problem is the safety of the graft and its possibility of being infected with diverse latent viruses that do not trigger a manifestation of the disease under normal conditions. ADSCs harvested from a dog`s omentum with canine distemper disease were found to be infected with canine morbillivirus [ 103 ]. In this study, before the clinical use of ADSCs, cells were checked for the presence of latent viruses.

Currently, the major dilemma with fat grafting, as well as with other biological grafts and substances, is inconsistent results of experimental and clinical findings attributable to poor standardization resulting from wide varieties of harvesting methods, donor sites, and patients’ initial state of health, as well as a lack of established, objective methods for assessment of clinical results and a lack of knowledge on the precise mechanism of stem cell action and regenerative mechanisms. There is also a lack of data and evidence from which to draw conclusions regarding the safety, effectiveness, and impact of ADSCs and other adipose tissue grafts on tissue regeneration [ 104 ].

The main issues that should undergo standardization are adipose tissue harvesting and processing, donor`s health condition, age, cryopreservation and storage procedure, freezing media that was used, quantification of ADSCs number and their phenotypical markers, storage duration, dosage used for the treatment of particular disease etc. Moreover, apart from three main widely accepted criteria for ADSCs – such as plastic-adherent during culturing, trilineage mesenchymal differentiation and expression of specific cell-surface antigens – a functional analysis of ADSCs properties (doubling time, specter and quantity of cytokines secretion, migration speed etc.) should be checked and compared to some standard in order to receive a predictable treatment result.

2.7 Latest clinical studies implementing ADSCs

The number of clinical trials using MSCs has recently significantly increased owing to notable successes and breakthroughs in basic research and experimental studies. New properties and clinical actions of MSCs have been discovered, and their clinical applications and indications have broadened.

Clinical studies have shown that infusion of MSCs leads to vigorous anti-inflammatory effects characterized by lymphocytosis and a decrease in levels of overactivated pro-inflammatory immune cells and TNF-α, in contrast to upregulation of IL-10 secretion. MSCs are known to auto-induce and address their microenvironment to promote cell proliferation and tissue regeneration. MSCs act via paracrine effects on cells and the organ environment, reducing cytokine storms and severe inflammation [ 105 ]. MSCs have been shown to demonstrate antimicrobial properties, increasing the immune response through the production of bactericide peptides and proteins, and the expression of indoleamine 2,3-dioxygenase (enzyme that decrease reproduction rate of viruses, some mammalian cells) and IL-17 [ 106 ]. ADSCs have proven efficient in the treatment of pulmonary diseases in vivo targeting a paracrine pathway, through the promotion of the epitheliocytes mitosis and apoptosis suppression [ 107 ]. The outcomes and limitations of clinical and randomized clinical trials with adipose tissue grafting products are shown in Table 2 .

3 Conclusion

The last five years have witnessed a huge breakthrough in the translation of basic research and experimental studies of ADSCs into clinical practice. ADSCs are the most promising and easy-to-obtain cells when compared to other MSCs because of their satisfactory cultural, biological, and clinical properties. The future of ADSC-based therapies likely belongs to allogeneic ADSCs. ADSC-based treatment is a highly promising method that utilizes etiological treatment approaches for diseases that are accompanied by cell death or acute tissue loss such as diabetes mellitus type I, xerostomia, periodontitis, and wound treatment for them stem cell therapy. ADSCs act through their differentiation to the specific type of mature cells which are determined by the particular microenvironment or cell stimuli or via paracrine regulation, such as secretion of growth factors and cytokines.

The clinical translation of ADSCs requires proper validation in large controlled trials, discovery of the exact mechanism of action, research standardization, and the adoption of pre-determined therapeutic guidelines.

The vast majority of pre-clinical in vivo studies showed positive treatment outcomes, however there were only a few clinical trials performed, indicating that enough clinical evidence is not yet available to allow broad ADSCs implementation into clinical practice. Among the limiting factors are: small patient sample sizes, predominantly short-term observation time, a lack of adipose tissue graft standardization (procedure and cite of the graft harvesting, cell culturing protocols), deficit of clinical protocols and guidelines, and subjective scoring methods for clinical study results (such as visual assessment and patients’ response to pain) [ 22 ]. Currently, most preclinical studies and clinical trials reported that ADSCs are relatively safe and effective [ 104 , 115 ]. The issue of dose, quality of the graft and indications remains unresolved and debatable [ 62 ]. According to the reviewed literature analysis, adipose tissue-derived biological products such as (ADSCs, SFV, ADSCs-Ev) showed promising results in clinical and in vivo setting. However, one of the main limitations of ADSCs therapy, as all other biologics-based drugs, is a process of the graft standardization. Such standardization should take into consideration the functional properties of the graft, such as doubling time, specter and quantity of cytokines secretion, migration speed etc., all of which varied based on the procedure of their isolation, localization, and pretreatment used in order to provide predictable and effective treatment outcomes.

Data availability

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Availability of data and materials

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Abbreviations

Adipose-derived stem cells

Mesenchymal stem cell

Bone marrow-derived stem cells

Regenerative medicine

Visceral adipose tissue

White adipose tissue

Stromal vascular fraction

Extracellular vesicles

Vascular endothelial growth factor

Transforming growth factor

Matrix metalloproteinase

Tumor necrosis factor

Platelet-rich plasma

Interferon gamma

Peroxisome proliferator-activated receptor gamma

Nuclear factor kappa-light-chain-enhancer of activated B cells

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Skrypnyk, M. Current progress and limitations of research regarding the therapeutic use of adipose-derived stem cells: literature review. J.Umm Al-Qura Univ. Appll. Sci. (2024). https://doi.org/10.1007/s43994-024-00147-9

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Published: 25 March 2023

Functionalization of curcumin nanomedicines: a recent promising adaptation to maximize pharmacokinetic profile, specific cell internalization and anticancer efficacy against breast cancer

Jinku Zhang 1 ,
Jirui Sun 1 ,
Chong Li 2 ,
Haizhi Qiao 1 &
Zahid Hussain 3 , 4

Journal of Nanobiotechnology volume 21 , Article number: 106 ( 2023 ) Cite this article

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Owing to its diverse heterogeneity, aggressive nature, enormous metastatic potential, and high remission rate, the breast cancer (BC) is among the most prevalent types of cancer associated with high mortality. Curcumin (Cur) is a potent phytoconstituent that has gained remarkable recognition due to exceptional biomedical viability against a wide range of ailments including the BC. Despite exhibiting a strong anticancer potential, the clinical translation of Cur is restricted due to intrinsic physicochemical properties such as low aqueous solubility, chemical instability, low bioavailability, and short plasma half-life. To overcome these shortcomings, nanotechnology-aided developments have been extensively deployed. The implication of nanotechnology has pointedly improved the physicochemical properties, pharmacokinetic profile, cell internalization, and anticancer efficacy of Cur; however, majority of Cur-nanomedicines are still facing grandeur challenges. The advent of various functionalization strategies such as PEGylation, surface decoration with different moieties, stimuli-responsiveness (i.e., pH, light, temperature, heat, etc.), tethering of specific targeting ligand(s) based on the biochemical targets (e.g., folic acid receptors, transferrin receptors, CD44, etc.), and multifunctionalization (multiple functionalities) has revolutionized the fate of Cur-nanomedicines. This study ponders the biomedical significance of various Cur-nanomedicines and adaptable functionalizations for amplifying the physicochemical properties, cytotoxicity via induction of apoptosis, cell internalization, bioavailability, passive and active targeting to the tumor microenvironment (TME), and anticancer efficacy of the Cur while reversing the multidrug resistance (MDR) and reoccurrence in BC. Nevertheless, the therapeutic outcomes of Cur-nanomedicines against the BC have been remarkably improved after adaptation of various functionalizations; however, this evolving strategy still demands extensive research for scalable clinical translation.

Graphical Abstract

Introduction

Breast cancer: prevalence, signs and symptoms.

Owing to its aggressive nature, heterogeneity, and immense potential to metastasize to other body organs (i.e., lung, liver, bone, brain, skin, etc.), breast cancer (BC) is among the most prevalent types of cancer that occurs in women (also in men). According to an estimate, approximately 458,000 deaths and more than one million new cases of BC happen each year globally [ 1 , 2 ]. According to the American cancer society, the most common type of cancer diagnosed in the American women between 2019 and 2021 was BC with an increased incidence rate of 0.5% per year and after the lung carcinoma it was second most prominent cause of mortality in women. Similarly, according to a recent statistical estimation of International Agency for Research on Cancer (IARC) which is a part of World Health Organization (WHO) of United Nation (UN), BC was the first among the five most diagnosed types of cancer in UAE with 1030 new cases in 2020 [ 3 , 4 ]. Various signs and symptoms of BC include obvious changes in the size and/or shape of the nipple or breast, presence of lump(s) or swelling in the breast, severe consistent pain in the breast, nipple area or underarm (armpits), discharge of blood from the nipple, and redness or flaky areas around the nipple or on the breast [ 5 ]. If one or more than one such signs or symptoms appear in a patient, it is highly recommended to immediately perform certain tests including the breast ultrasound, mammogram, magnetic resonance imaging (MRI), and/or breast biopsy for further investigation and diagnosis.

It has been established that the overall survival rate and diseases free survival are higher when BC is diagnosed at early stages or treated with rational therapeutic regimen [ 4 , 5 ]. Detection of BC at early stages leads to better therapeutic outcome: 5-year survival rate is 100% when BC diagnosed at stage I in comparison with only 20% survival rate when BC diagnosed at advanced stages (stages III and IV) [ 6 , 7 ].

For rationalizing a most suitable therapeutic regimen for BC patient, staging is critically important. Staging also helps to; (1) assess the therapeutic success of prescribed medication and results of clinical trials, (2) evaluate the survival statistics, (3) exchange or compare the medical information between the various treatment centers, and (4) serves as a baseline for translational research. For staging of BC, TNM system (Tumor, Lymph node, Metastasis) of American Joint Committee on Cancer (AJCC) is most employed (Table 1 ). The stage of the BC is usually determined from characteristics of the cancer such as how big is the lump/tumor, extent of metastasis (local or distant), and involvement of hormone receptors (i.e., estrogen, progesterone, and HER2 status) (Fig. 1 ) [ 4 ].

Stages T1–T4 describe the size of tumor and extent of metastasis to the chest wall (local metastasis) or to other organs of the body (distant metastasis). Created with BioRender.com

Stage I: (T1, N0, M0).

Stage II (A, B).

IIA: 1) T0, N1, M0; 2) T1, N1, M0; 3) T2, N0, M0.

IIB: 1) T2, N1, M0; 2) T3, N0, M0; 3) T3, N1, M0.

Stage III (A, B, C).

IIIA: 1) T0, N2, M0; 2) T1, N2, M0; 3) T2, N2, M0; 4) T3, N2, M0; 5) T3, N1, M0.

IIIB: 1) T4, N0, M0; 2) T4, N1, M0; 3) T4, N2, M0.

IIIC: 1) Any T, N3, M0.

Stage IV: Any T, any N, M1.

Pathophysiology and risk factors

Owing to its intricate and multi-factorial nature, pathophysiology of BC is not fully understood yet; however, probability of developing BC could be enhanced due to certain risk factors. For example, old age is one of the major risk factors for developing the BC. Females aged below 25 rarely develop BC; however, women with age 50–69 have higher probability of developing BC [ 8 , 9 ]. Other risk factors include genetic mutation (e.g., BRCA 1 and 2) that accounts for about 10% of BC cases [ 10 ], induction of P53, overexpression of cyclin D gene [ 11 ], higher body mass index (BMI), late pregnancy (> 30 years) [ 12 ], family history of BC or other non-cancerous breast diseases, any previous treatments involving radiations, late menopause, and postmenopausal hormone replacement therapy (HRT) [ 13 ].

Metastasis to other organs

The spreading of BC from the primary site (i.e., breast duct) to the surrounding healthy cells/tissues (local metastasis) or to other organs of the body (e.g., bones, brain, lungs, liver, etc.) (distant metastasis) is called metastasis. Following its metastasis, it is still considered as BC because the cells (also called as circulating tumor cells) which metastasize to other organs are BC cells which broke away from the original tumor and invaded to nearby tissues or travelled through the bloodstream or lymph nodes to the distant organs. Stage IV BC, also called as metastatic BC, is characterized by distinct spreading to other organs and thus the overall survival rate in these patients is usually below 20%. Even months after the successful treatment of BC, it can still reappear in other organs which is called metastatic reoccurrence or distant reoccurrence.

Metastasis is a complex interplay involving the multiple cellular processes including the hyperproliferation (cell division) of primary tumor cells, cell invasion through the basement membrane to the surrounding tissues, cell intravasation to the bloodstream or lymphatic system, cell migration through blood circulation or lymph, extravasation from the bloodstream via trans-endothelial migration, invasion into the distant organs, multiplication or cell division into distant tissue, and formation of distant tumor (Fig. 2 ). The organs that are commonly metastasized with BC include the brain, liver, bones, and lungs. Over 10–30% of patients with metastatic BC contain BC cells invaded into the brain tissues. Symptoms of metastatic BC depends upon the organ to which it has been metastasized. For example, severe progressive generalized pain, profound fatigue, and easily fractured bone are the typical symptoms of BC metastasized to bone tissues. Progressive headache, dizziness, vomiting, nausea, seizures, and visual disturbance are typical signs of BC metastasized to the brain. Abdominal pain, nausea, vomiting, stomach swelling, increase in the liver enzymes, and jaundice indicates metastasis of BC to the liver.

BC metastasis to brain tissues (distant metastasis). Created with BioRender.com

Conventional treatments for BC and limitations

There are three most exploited conventional strategies for the management of BC such as chemotherapy, surgery, and radiation therapy; however, other techniques can also be used in some cases such as personalized medicine, immunotherapy, hormonal therapy, and bone marrow transplant.

Chemotherapy

Chemotherapy involves the administration of anticancer drugs that can effectively target and destroy the BC cells with minimal toxicity to the surrounding healthy cells. Anticancer drugs are frequently administered intravenously (injection) or through oral route (pills or tablets). Depending upon the patient condition and the cancer stage, chemotherapy is administered alone or in combination with other therapies such as radiation, surgery and/or hormonal therapy. Chemotherapeutic drugs help relieving the symptoms, mitigating the spread, and preventing the reoccurrence of BC. As an adjuvant, chemotherapeutic agents can also be given to BC patients after undergoing the surgical intervention for complete eradication of the remaining cancerous cells as well as preventing the reoccurrence of the disease [ 14 ]. Chemotherapeutic agents can also be administered before carrying out the surgery in BC patients in order to shrink the tumor size thus decreasing the severity and extent of surgical excision. This therapy is called neo-adjuvant chemotherapy or preoperative chemotherapy. Neo-adjuvant chemotherapy is usually recommended in HER2 positive BC, triple negative BC, inflammatory BC, BC metastasized to lymph nodes, and all advanced stage BC (stages III and IV). In advanced stage BC, chemotherapy is the first-choice treatment modality because BC has metastasized to other organs (e.g., lungs, liver, bone, brain, etc.) of the body and thus surgery is not the viable option in these situations. Often a combined chemotherapeutic regimen involving the multiple chemotherapeutic agents are recommended for such patients to improve their quality of life, disease free survival, and overall survival rates [ 15 ].

According to EBCTCG studies, adjuvant or neo-adjuvant chemotherapy or a combined chemotherapy significantly decrease the mortality rate and probability of recurrence of BC, particularly in patients with age less than 50. Administration of cyclophosphamide with methotrexate and 5-fluorouracil shows major effectiveness in node-positive tumors in premenopausal patients. The use of anthracyclines has shown good efficacy as combination chemotherapy regardless of menopausal status as well as in ER-positive tumors [ 16 ]. Another chemotherapeutic agent which has shown good efficacy against ER-positive tumor is tamoxifen; however, according to some clinical trials, use of chemo-endocrine therapy showed more significant effects compared to tamoxifen alone. A common regimen practiced in the United States is the combination of doxorubicin and cyclophosphamide as four cycles, followed by four cycles with paclitaxel. The dose–dense (dd) AC-T is administered along with growth factor every 2 weeks [ 17 ]. According to a meta-analysis, there is a significant benefit of chemotherapy for patients with HR-negative BC in decreasing the recurrence and mortality [ 18 ].

Chemotherapeutic agents are also commonly recommended as systemic adjuvant therapy to BC patients after the surgical resection to eradicate micro-metastatic tumor that might progress at later stages (if not treated properly). The criterion of the selection of an adjuvant therapy is based on BC burden including the number of lymph nodes involved, primary tumor size and the pathophysiology involved. Patients with triple negative and HER2 positive cancers are at higher risk, although in HR positive and HER2 negative cancers there is a biological diversity. According to a clinical trial, HR positive, HER2 negative, and node-negative BC cases have shown good response to chemotherapy as an adjuvant therapy [ 19 ].

Another standard regimen is docetaxel with AC (DAC), although there is an increased risk of toxicity and febrile neutropenia with docetaxel use [ 24 ]. According to the trial data from Cancer and Leukemia Group B and US Breast Cancer Intergroup, chemotherapy shows decrease in the relative risk in HR negative cancer patients about 21–25%, and HR positive cancer of 8–12% relative risk reduction. Oncotype DX gives an estimation about the benefits of chemotherapy where higher oncotype recurrence above 31 shows greater risk reduction of recurrence with chemotherapy [ 20 ].

HER2 targeted therapy can also be given in combination with chemotherapeutic agent to HER2 positive BC patients. Results of this randomized trial indicated that trastuzumab combined with chemotherapy against HER2 receptor BC patients have shown 50% reduction in the recurrence rate [ 21 ]. Similarly, trastuzumab has been given in combination with paclitaxel to stage I HER2 positive BC patients, while stage II-III HER2 positive BC patients were given trastuzumab with AC-T or docetaxel and carboplatin. Pertuzumab which is a HER2 dimerization inhibitor has also shown good anti-BC response when administered in combination with trastuzumab [ 22 ].

Limitations of chemotherapy

Though, chemotherapy is considered as a first-choice therapy for advanced stage BC, either alone or in combination with other therapeutic modalities; however, several limitations including poor selectivity which results in promising cytotoxicity in normal healthy cells having high proliferation rate limits its clinical significance. Another limitation associated with chemotherapy is “multidrug resistance (MDR)”. One of the mechanisms of MDR is overexpression of efflux pump (P-glycoprotein) which results in efflux (pumping-out) of the internalized anticancer drug and thus results in decreased intracellular levels of anticancer drugs. Another limitation is low aqueous solubility as many chemotherapeutic agents derived from plant or synthetic sources are hydrophobic in nature [ 23 , 24 ]. Chemotherapy also cause several adverse effects such as alopecia, cognitive and sexual dysfunction, persistent nausea and vomiting, amenorrhea, menstrual pain, and bone pain that affects patient’s quality of life [ 25 ]. These limitations reduce therapeutic significance and patient compliance with chemotherapy.

Surgery is the most adaptable treatment modality for the removal of localized tumors [ 26 ]; however, for better therapeutic outcomes, surgery can be adjuvant with radiation or chemotherapy prior to undergoing surgical procedure to shrink the tumor size. The adjuvant therapy can also be recommended after the patient has undergone the surgical procedure to mitigate the risks of reoccurrence and to kill remnants of cancerous tissues. Two main surgical approaches are usually adopted: first is called as breast-conserving surgery in which only the cancerous part of the breast (lumpectomy) or the cancerous part along with the rim of nearby healthy tissues (wide excision) are surgically removed (Fig. 3 ). Quadrantectomy, also called as partial mastectomy, is also a type of breast-conserving surgery in which one quarter of the breast tissues is removed along with the muscles of chest wall within 2–3 cm radius of the tumor. This is usually an out-patient surgery which takes about 1–2 h and patient can be discharged after the surgery; however, surgeons should take into consideration several factors such as breast size, tumor size, underlying health condition, BC staging, and extent of metastasis [ 27 ]. Lumpectomy is normally recommended for patients with early stages (stage I or II). The second surgical approach is mastectomy which involves a complete removal of the breast and can be done on both sides. Generally, it is recommended for stages III and IV patients where tumor size is greater than 5 cm size. Sometime, mastectomy is recommended in case of BC recurrence after a patient has undergone breast-conserving surgery [ 28 ].

Surgical approaches for treatment of BC. Created with BioRender.com

Limitations of surgical approach

After undergoing surgical procedure particularly in case of breast conserving surgery, radiation therapy is highly recommended to minimize the risks of recurrence; however, patient must be a good candidate for the radiation therapy otherwise it may result in severe adverse events on follow-up. Moreover, tumor tissue should be excised to negative margins and should obtain good cosmetic results. Other limitations of the surgical approach include formation of calcification in the breast with malignancy features, inability to remove tumor within negative margins, high risk of recurrence, and invasiveness (permanent removal of whole breast with additional reconstructive surgeries particularly after the mastectomy) [ 27 , 28 ]. These limitations reduce the clinical acceptability of surgical invention for the treatment of BC.

Radiation therapy

Radiation therapy involves the application of high doses of ionizing radiations directed to the cancerous tissues/cells. It is often recommended in combination with other conventional treatments. For example, prior to the surgical procedure radiotherapy can decrease the size of the tumor or post-mastectomy radiotherapy is recommended to decrease the risks of recurrence as well as to improve patient’s quality of life and overall survival rates [ 29 ]. The ionizing radiations induce deterioration in genetic material of BC cells which ultimately stop their proliferation via arresting the cycle, migration and metastasis and promote their apoptosis. Generally, conventional radiotherapy requires twenty-five to thirty sessions depending upon the patient condition and BC staging. Alternatively, hypofractionation radiation therapy involves lesser number of sessions (thirteen to sixteen) but with higher doses; however, it depends on various patient’s factors such as age and metastatic status as well as the inferior therapeutic outcomes (e.g., low reconstruction or cosmetic benefits) [ 30 ].

Limitations of radiotherapy

Radiation therapy is considered as an essential modality for cancer treatment with substantial significance; however, it is associated with several problems such as damage to non-cancerous surrounding tissues/cells which results in chromosomal abnormalities and structural changes in patients receiving the radiotherapy. Several other side effects have also been reported in BC patients undergone radiotherapy such as skin irritation and dermatitis, heaviness and swelling, appearance of discoloration or bruised skin, and edema of the lymph. Moreover, various medical conditions (e.g., scleroderma; a connective tissue disease) that increases sensitivity of the skin may happen in patients undergoing the radiotherapy [ 31 ].

Curcumin (Cur)

To search for alternative viable options for treatment of BC, many researchers have exploited the biomedical efficacy of naturally originated constituents. Curcumin (Cur) is one of the most-studied natural compounds with a wide range of biomedical applications. Cur is a hydrophobic polyphenol extracted from an herbal dietary spice “Turmeric” which is derived from the rhizome of Curcuma longa . Cur has been well-exploited against different types of ailments including the cancer [ 32 , 33 , 34 ], wound healing [ 35 , 36 ], anti-inflammatory [ 37 , 38 ], antimicrobial [ 39 ], antioxidant, antipyretic, bone diseases, and many other illnesses.

In addition to its traditional uses for the management of various health conditions, Cur has shown tremendous anticancer potential against different types of cancer including the BC [ 40 ]. Cur is a potent anticancer agent alone or in conjunction with other conventional anticancer therapies such as radiotherapy [ 41 , 42 ], surgical intervention [ 43 ], or chemotherapy [ 44 ]. When employed in conjunction with radiotherapy, Cur acts as a potent radiosensitizer for the BC cells/tissues and radioprotector for the normal healthy cells. Though the data is scarce, but many studies have reported that potency and effectiveness of radiotherapy was markedly improved when used in combination with Cur (through oral, subcutaneous, or IV routes) [ 41 , 42 ]. It has also been proposed that administration of Cur in BC patients prior to the surgical procedure causes shrinkage of the tumor size which results in better therapeutic outcomes [ 43 ]. Moreover, substantial data exist in the literature which establishes the synergistic efficacy of Cur and chemotherapy with significant improvement in patient’s quality of life, overall survival rate, low remission, and poor chemoresistance [ 44 ]. The anticancer effects of Cur can be attributed to its potent anti-proliferative potential against the BC cells by arresting their cell cycle (G2/M) and induction of apoptosis (p53-dependent) [ 45 , 46 ]. Downregulation in the expression of EZH2 gene (enhancer of zeste homolog-2) via the mitogen-activated protein kinase (MAPK) pathway also contributes to the anticancer effect of Cur [ 47 , 48 ]. Cur has also shown an exceptional ability to suppress the proliferation, migration, and invasion in BC cells via the repression of NF-κB [ 49 ] and/or downregulation of miRNA-34a that is responsible for epithelial-mesenchymal transition in the tumor microenvironment [ 50 ]. Inhibition of Ki-67, proliferating cell nuclear antigen (PCNA), and Bcl-2 as well as upregulation of P-53 mRNA expression and induction of Bax mRNA expression have also been observed in BC cells treatment with Cur [ 40 ]. In addition, prevention of angiogenesis (neo-vascularization) due to downregulation of vascular endothelial growth factor (VEGF) has also been evidenced as chemotherapeutic mechanism of Cur against the BC [ 51 , 52 ].

Despite promising anticancer potential, product development for Cur is hampered due to low aqueous solubility, chemical instability, photo-degradability, low bioavailability [ 53 ], rapid metabolism [ 54 ] and short-plasma half-life [ 55 ], and vulnerability to auto-oxidation [ 56 , 57 ]. To overcome these challenges, nanotechnology has been deployed for product development of Cur and results have shown revolutionary improvement in the physicochemical properties (e.g., aqueous solubility), chemical stability, and biomedical efficacy of Cur for the management of BC.

Nanotechnology: Nanoencapsulation of Cur

Nanotechnology deals with the synthesis, characterization, and application of nano-scaled materials (1–1000 nm). The deployment of nanotechnology in medicine has shown tremendous potential for early and accurate diagnosis as well as for rational treatment of various diseases including the cancer. The extensive research has been carried out on nanotechnology to improve aqueous solubility, absorption, permeation, bioavailability, and anticancer efficacy of Cur [ 58 , 59 , 60 , 61 , 62 , 63 , 64 ]. It has been established that a good control over the physicochemical properties (i.e., size, zeta potential, thermodynamics, morphology, and colloidal stability) of Cur-based nanomedicines is mandatory for improving its pharmacokinetic profile and anticancer efficacy [ 62 , 63 ]. Nevertheless, the nanoencapsulation of Cur has significantly improved its internalization, cell uptake, and anticancer efficacy against BC [ 64 ]; however, one of the major limitations of these nanomedicines is lacking of selective targeting which still cause severe side effects. To mitigate this issue, several adaptations have been made in the design of nanomedicines which significantly improve specific targetability of these delivery systems to the tumor microenvironment. Nanomedicines can target the tumor tissues by (a) passive targeting and/or (b) active targeting (Fig. 4 ).

Active and passive targeting approaches for Cur-nanomedicines. Created with BioRender.com

Passive targeting

Passive targeting refers to preferential accumulation of Cur-nanomedicines into neoplastic tissues as result of enhanced permeability and retention (EPR) [ 65 ]. The passive accumulation of Cur-nanomedicines into tumor microenvironment (TME) can be obtained by optimizing their physicochemical features such as ultra-fine particle size, hydrophilic exterior, good zeta potential, and surface functionalization with various hydrophilic moieties. Another important factor which is responsible for the passive targeting of Cur-nanomedicines into TME is the leaky vasculature of tumor tissues due to irregularly arranged endothelial cells in the newly formed blood vessels because of the abnormal angiogenesis [ 66 , 67 ]. One of the reasons for the leaky vasculature is an imbalance between the supply and demand of the nutrients to the growing and proliferating BC cells. Cancer cells proliferate in an uncontrolled manner and thus have huge demand of nutrients supply which surpass the ability of our body. This results in formation of immature and leaky vasculature and improperly arranged endothelial cells which ultimately results in enhanced permeation of Cur-nanomedicines into the tumor tissues from the blood circulation. In addition, the cancerous tissues are unable to build mature and proper lymphatic system which results in poor drainage of the permeated nanomaterials and as a result prolongs the retention of Cur-nanomedicines into the cancerous tissues.

Active targeting

Active targeting refers to the selective delivery of drugs to the specific cells/tissues of the body [ 68 ]. To achieve this, various targeting ligands (i.e., peptides, antibodies, folic acid, hyaluronic acid, etc.) can be conjugated on the surface of Cur-nanomedicines which facilitate the recognition of specific substrate receptors (e.g., folate, CD44, transferrin receptors, etc.) that overexpress on the surfaces of tumor cells [ 68 ]. The specific interaction of targeting ligand conjugated nanomedicines with receptor-bearing cancer cells results in a selective accumulation of Cur-nanomedicines into the target cancer cells/tissues and as a result off-target accumulation of chemotherapeutic payload is averted (Fig. 4 ).

Cur-nanomedicines and adaptable functionalizations

In the recent decades, a variety of nanodelivery systems have been engineered for improving the physicochemical properties, pharmacokinetic profile, biodistribution (via passive or active targeting), and anticancer efficacy of Cur against the BC (Fig. 5 ). Nonetheless, some Cur-nanomedicines have satisfactorily addressed almost all the issues associated with the Cur; however, majority of Cur-nanomedicines are still facing grandeur challenges particularly in in vivo settings and in humans, which restrict their clinical translation. Therefore, in the following sections, we have critically discussed a variety of Cur-nanomedicines as well as diverse adaptations (functionalizations) that have been carried out in the architecture of Cur-nanomedicines to mitigate challenges and to further improve their pharmacokinetic profile and anticancer efficacy against the BC.

Types of Cur-nanomedicines exploited for the treatment of BC. Created with BioRender.com

Owing to their structural resemblance (lipid bilayer) with the biological membranes, liposomes (spherical-shaped nanovesicles) have been extensively employed as a drug nanocarrier for a wide variety of drugs [ 69 , 70 , 71 ]. Moreover, the high encapsulation efficiency, ability to encapsulate the hydrophilic and hydrophobic drugs simultaneously, biocompatibility, good permeation efficiency, sustained release characteristics, and high flexibility of modulation make liposomes an efficient delivery vehicle. Due to their unique architecture and physicochemical features, liposomes have significantly improved aqueous solubility, absorption, bioavailability, biodistribution, and anticancer efficacy of Cur. However, recent developments including the conjugation of targeting ligand(s) on the exterior surface of the liposomes for active targeting, PEGylation (stealthing) to prolong the plasma half-life, and incorporation of the pH-sensitive linker have profoundly improved the pharmacokinetic profile and anticancer efficacy of Cur [ 70 , 71 ].

Hasan and colleagues [ 72 ] fabricated the Cur-encapsulated liposomes and evaluated for physicochemical properties and cytotoxicity against the BC cells (MCF-7). A dose dependent increase in the cytotoxicity was observed in MCF-7 cells treated with Cur-liposomes compared to the free Cur. The cytocompatability test against the normal breast epithelial cells (MCF-10A) validated that Cur-liposomes showed no signs of cytotoxicity in these non-malignant breast cells which indicated their selectivity towards BC cells. The anticancer efficacy of Cur-loaded liposomes was attributed to upregulated production of reactive oxygen species (ROS) and substantial damage to essential sub-cellular structures (i.e., DNA, RNA, and proteins) of the BC cells [ 72 ]. Furthermore, the surface functionalization of Cur-liposomes with salmon's lecithin showed superior anticancer efficacy compared to the Cur-liposomes without surface functionalization as well as the ones functionalized with rapeseed and soya lecithins. These results concluded that functionalization of Cur-liposomes with salmon's lecithin significantly improves their properties and selective targeting of BC cells [ 72 ]. Same research group later also reported that physicochemical properties, colloidal stability, oral bioavailability, and anticancer efficacy of Cur against the BC can be further augmented via the functionalization of Cur-liposomes with chitosan (CS) [ 73 , 74 ]. Likewise, the complexation of Cur with β- or γ-cyclodextrin (CD) with subsequent encapsulation into the liposomes was also found promising strategy for increasing the aqueous solubility and physicochemical stability of the Cur [ 75 , 76 ].

Active targeting strategy has gained an exceptional recognition in mitigating the off-target effects of the chemotherapeutic agents. Therefore, extensive research has been carried out on functionalization of liposomes with specific ligands such as folic acid (FA) [ 77 ]. In this study, the specific cell uptake efficiency of FA-functionalized Cur-liposomes was evaluated using the malignant triple negative BC cells (MDA-MB-231) compared to the non-malignant breast cells (MCF-12A). A strong fluorescence observed in the MDA-MB-231 cells compared to the MCF-12A cells indicated the selective targetability of FA-functionalized liposomes which was attributed to overexpressed FA-receptors on the surface of malignant BC cells [ 77 ]. It has also been reported that LD 50 of Cur-liposomes in MDA-MB-321 was around 19 µM which was significantly lower than LD 50 observed in MCF-12A. Conclusively, FA-conjugation of Cur-liposomes can be a promising adaptation to improve the specific targetability and anticancer efficacy of Cur-liposomes against the BC [ 78 ]. The selective targeting efficiency of FA-functionalized Cur-liposomes has also been investigated by the Luiz and colleagues [ 79 ]. The fabricated liposomes exhibited ultra-fine particle size (138 nm), good encapsulation efficiency (⁓73%), and smooth spherical morphology. FA-functionalized Cur-liposomes (LIP-CCM-FA) also displayed a significant improvement in the cytotoxicity, cell uptake efficiency (higher fluorescence), and anticancer efficacy against the BC cells (MCF-7) compared to the unfunctionalized Cur-liposomes and the plain Cur (Fig. 6 ) [ 79 ].

Cell uptake efficiency of FA-functionalized CUR-loaded liposomes (LIP-CCM-FA): A confocal images of MCF-7 taken at 24 h post-incubation, B fluorescence intensity, and C cell uptake efficiency. Adapted from [ 79 ]

Another biochemical target that has been well-studied by the researchers for selective targeting of BC cells is the human epidermal growth factor receptor-2 (HER-2). Hence, many researchers have designed unique Cur-liposomes for specific targeting of HER-2 receptors overexpressed on the surface of BC cells. Moballegh-Nasery et al. [ 80 ] engineered affibody-decorated Cur-loaded liposomes and investigated anticancer efficacy against the BC cells (SKBR3 and MCF7). The fabricated Cur-loaded liposomes (⁓150 nm) displayed a significant improvement in cell uptake efficiency, cytotoxicity (via induction of apoptosis), and anticancer efficacy [ 80 ]. Another functionalization for improving the aqueous solubility, oral absorption, and anti-tumor efficacy of Cur against the BC was surface coating of Cur-loaded bilosomes with D-alpha-tocopherol polyethylene glycol succinate (TPGS) (TPGS-Cur-Bil) [ 81 ]. The fabricated TPGS-Cur-Bil possessed the nanoscaled size (⁓190 nm), narrow PDI (0.26), good zeta potential (−41 mV), high encapsulation efficiency (93%) and good storage stability. In comparison to the free Cur and unfunctionalized liposomes (Cur-Bil), the TPGS-Cur-Bil displayed a significantly higher cell uptake efficiency and cytotoxicity against the MCF-7-ADR which evident the promising potential of TPGS-functionalization for specific targeting and enhanced anticancer efficacy against the breast cancer [ 81 ].

Hybrid nanomaterials which are unique chemical conjugates of different materials have gained special recognition due to their diverse unique properties. Ruttala et al. [ 82 ] designed a novel hybrid PEGylated liposome encapsulated with PTX-loaded albumin-NPs and Cur. The purpose of this hybrid nanosystem was simultaneous delivery and sustained release of two chemotherapeutic agents (PTX and Cur) for the synergistic anticancer efficacy. The developed PEGylated hybrid liposomes were characterized and evaluated for cytotoxicity against BC cells (MCF7 and B16F10), cell uptake efficiency, and antineoplastic efficacy. The PEGylated hybrid liposomes exhibited nanoscaled dimension (⁓200 nm), narrow PDI, high encapsulation efficiency (99%), smooth spherical morphology, and good storage stability in the serum at both 4 °C and 25 °C. The Cur-PTX co-encapsulated hybrid liposomes (CL-APN) also displayed a significant increase in the cell uptake efficiency with subsequent reduction of cell migration in both BC cells (MCF7 and B16F10) compared to the plain Cur-liposomes and the pure Cur. These results indicated that multi-functionalization is the recent-most adaptation for improving the anti-proliferative and anti-metastatic efficacy of Cur-liposomes for the treatment of BC [ 82 ].

As functionalization of Cur-nanomedicines is relatively a newer adaption, therefore, no patent is granted yet to these nanoformulations. However, a patent was granted to Kurzrock and colleagues on Cur-loaded liposomal formulation developed for the treatment of a variety of cancers including the BC, pancreatic cancer, and melanoma [ 83 ]. The encapsulation of Cur into the liposomes resulted in a significant improvement in the aqueous solubility, chemical stability, cytotoxicity, cell uptake, antioxidant potency, and anticancer efficacy against different types of cancer including the BC [ 83 ].

Solid lipid nanoparticles (SLNs)

Due to their ultra-fine particle size, composition, thermodynamic stability, biocompatibility, and flexibility of modulation, the SLNs can significantly improve delivery of anticancer drugs. Nanoencapsulation of drugs into SLNs provides good protection against the chemical degradation and hence improves the storage stability of Cur [ 84 ]. SLNs also prolong the blood circulation time which result in improvement of pharmacokinetic profile, biodistribution to cancer tissues (via EPR effect), and improvement of therapeutic efficacy [ 85 , 86 ]. Rahman and colleagues [ 84 ] developed the SLNs encapsulated with niclosamide (an oral anthelminthic drug) for improving its aqueous solubility, absorption, and oral bioavailability. Results showed a sustained release of encapsulated drug (⁓93% release occurs within 12 h) and improved oral bioavailability (2.2-fold higher than the pure drug) [ 84 ]. Similarly, melphalan-loaded SLNs were developed for systemic delivery [ 85 ]. Results showed a promising improvement in the aqueous solubility, chemical stability, biocompatibility, and a sustained release profile. The drug loaded SLNs also displayed an extended half-life, lower plasma clearance, and higher drug retention into the target tissues [ 85 ]. Keeping in view of their great stability, affordability, scalability, and biopharmaceutical feasibility, many researchers have employed SLNs for targeted delivery of CUR for the treatment of BC.

The physicochemical properties, stability, and transcellular permeation of SLNs is greatly influenced by the type(s) and nature of the lipid ingredient(s) used to formulate the SLNs. Therefore, Wang and coworkers [ 86 ] developed Cur-encapsulated SLNs by using two lipids (stearic acid and lecithin) and evaluated their efficacy for treatment of BC (SKBR3 cells). The developed Cur-SLNs exhibited the spherical morphology with 30–50 nm size and −25.3 mV zeta potential which indicates good thermodynamic stability of these NPs. The cytotoxicity data showed an IC 50 of 18.78 µM of Cur-SLNs compared to the IC 50 of 28.42 µM of free CUR after 48 h incubation with SKBR3 cells which clearly reflects an enhanced anti-proliferative efficacy of Cur-loaded SLNs. The cell uptake study displayed a relatively higher internalization of Cur-SLNs (higher fluorescence intensity) into the SKBR3 cells compared to the free Cur. The higher cell uptake of Cur-SLNs was expected to be one of the reasons for superior anticancer efficacy of Cur-SLNs compared to the free Cur. The anti-proliferative effect of Cur-SLNs was attributed to arresting of cell cycle at G1/S phase [ 86 ] and inducing the apoptosis via generating the ROS which upregulates the apoptosis by damaging the mitochondrial membrane via depolarization [ 85 , 86 , 87 ]. Likewise, Bhatt et al. [ 88 ] fabricated Cur-SLNs using a single lipid (glyceryl monostearate) in the presence of Poloxamer 188 as a stabilizer (surfactant). They optimized the Cur-SLNs via the quality-by-design (QoD) approach and tested the anticancer efficacy of optimized Cur-SLNs against the BC cells. The fabricated Cur-SLNs exhibited nanoscaled particle size (⁓230 nm), narrow PDI, and good loading efficiency. The comparative analysis revealed that Cur-SLNs displayed a superior cell uptake efficiency, downregulated viability, and significantly higher apoptosis in BC cells (MDA-MB-231) compared to the free Cur [ 88 ]. The Cur-SLNs have also been synthesized using the cholesterol as the lipid ingredient in the presence of Poloxamer 188 [ 89 ]. The optimized Cur-SLNs exhibited ultra-fine particle size (⁓170 nm), narrow PDI, and high encapsulation efficiency. The Cur-SLNs displayed a significantly higher anticancer potential in terms of downregulation of cell viability, higher cell uptake, and enhanced apoptosis in the BC cells (MDA-MB-231) compared to the free Cur [ 88 ]. Similar findings have also been reported by other studies [ 90 , 91 , 92 ].

One of the prime challenges in the treatment of BC is its aggressive nature and greater metastatic potential. The radiotherapy is one the viable options for the management of local, inoperable, incompletely resected, and recurrent BC. Therefore, Minafra et al. [ 93 ] proposed a synergistic therapy with Cur-SLNs (as a radiosensitizer and synergistic molecule) and radiotherapy (2–9 Gy) for the superior therapeutic outcomes against the BC. The therapeutic potential of adjuvant therapy (Cur-SLNs + radiotherapy) was tested against the tumorigenic BC cells (MCF7 and MDA-MB-231) and the non-tumorigenic BC cells (MCF-10A). Interestingly, adjuvant therapy (Cur-SLNs + radiotherapy) displayed a significantly higher cytotoxicity (lower IC 50 ) compared to the radiotherapy alone or in combination with free Cur. These results were attributed to Cur-SLNs which enhanced the radio-sensitization of BC cells against the radiation therapy as well as enhanced antioxidant potency [ 93 ].

Active targeting approach has also been adapted in the design of SLNs for selective targeting of BC cells/tissues [ 94 , 95 , 96 ]. Like other phenotypic markers, transferrin receptors are also overexpressed on the surface of different types of cancer cell including the BC cells. Therefore, Mulik et al. [ 97 ] developed transferrin conjugated SLNs for targeted delivery of Cur-SLNs into BC cells. The prepared transferrin-conjugated Cur-SLNs exhibited an ultra-small particle size, good encapsulation efficiency, sustained release profile and a significant improvement in cell uptake into MCF7 cells compared to the unfunctionalized Cur-SLNs and the free Cur. These results concluded that conjugation of Cur-SLNs with transferrin could be a promising approach to maximize the selective targeting into BC cells with ultimately enhanced anticancer efficacy against the BC [ 97 ]. On the other hand, Pawar et al. [ 98 ] developed SLNs for the co-delivery of Cur and docetaxel (DTX) and functionalized with FA. Additionally, they coated the exterior surface of FA-Cur-SLNs with polyethylene glycol (PEG) to confer them immune-compatible and biocompatible. The PEGylation was aimed to avert the recognition of Cur-SLNs by the RES for prolonging the plasma circulation time. The fabricated FA-conjugated Cur/DTX-SLNs were investigated for cytotoxicity against the BC cells (MDA-MB-231 cells) and for cell uptake efficiency (MCF-7). The optimized FA-Cur/DTX-SLNs exhibited an ultra-fine particle size (180–250 nm), good zeta potential (−27.5 mV), high encapsulation efficiency (⁓72%), smooth spherical morphology, and biphasic release profile. The FA-conjugated SLNs also displayed significantly higher cell uptake efficiency and lower cell viability compared to the unfunctionalized Cur-SLNs and the free Cur. Moreover, better pharmacokinetic and biodistribution profile was evidenced in case of functionalized Cur-SLNs compared to the unfunctionalized Cur-SLNs and the free Cur [ 98 ]. These results were also in agreement with another study [ 99 ]. These findings indicate that surface functionalization of Cur-SLNs via PEGylation or conjugation of targeting ligand(s) enhance their cell uptake efficiency, cytotoxicity, pharmacokinetic profile, biodistribution, and anticancer efficacy against the BC.

Albumin-NPs

By considering their biocompatibility and non-antigenicity, the albumin NPs have been broadly used for treatment of different diseases including the BC. Like other nanocarriers, the physicochemical features (i.e., particle size, surface chemistry, entrapment capacity, morphology, and colloidal stability) of the albumin-NPs play an imperative role in their transmembrane permeability, biodistribution, and specific targeting to the TME. Keeping in view of the great potential of albumin NPs, Jithan et al. [ 100 ] developed albumin-NPs for improving the plasma circulation time, preferential distribution to TME, and the anticancer efficacy of Cur. The prepared Cur-loaded albumin-NPs exhibited a nano-scaled size (< 250 nm), good encapsulation efficiency (75%-85%), and highly sustained release kinetics (90% Cur was released in one month) [ 100 ]. The Cur-loaded albumin-NPs also displayed significantly higher cytotoxicity against the MDA-MB-231 cells compared to the free Cur. The superior pharmacokinetic profile, biodistribution to tumor tissues, and anticancer efficacy of Cur-loaded albumin-NPs has also been validated in vivo in comparison with free Cur [ 100 ].

To further improve their selective targeting and anticancer efficacy, a variety of functionalization methods have been adapted in the design of Cur-loaded albumin-NPs. Hasanpoor et al. [ 101 ] engineered Cur-loaded albumin-NPs (human serum albumin, HSA) and decorated their exterior surface with PDL-1 (programmed death ligand-1) to confer them with selective targeting feature. Like other phenotypic markers, BC cells also exhibit over-expression of PD-L1 which is largely responsible for evasion of immune response and thus contribute to chemoresistance in BC. The developed PDL-1 functionalized Cur-loaded albumin-NPs (P-HAS/Cur-NPs) were evaluated for cell uptake efficiency and dose dependent (12.5, 25, 35, and 50 μM) cytotoxicity against the BC cells (MCF-7 and MDA-MB-231) at different time periods (24, 48, and 72 h). The fabricated P-HAS/Cur-NPs exhibited 200–250 nm particle size with smooth spherical morphology. The cell cytotoxicity study depicted a time- and dose-dependent cytotoxicity in both the BC cells treated with P-HAS/Cur-NPs compared to the unfunctionalized albumin-NPs (HAS/Cur-NPs) and the free Cur. Interestingly, the IC 50 (61 μM) of P-HAS/Cur-NPs PD against the MDA-MB-231 cells at 72 h was fourfold less than the IC 50 (234 μM) obtained with free Cur which indicates a powerful cytotoxic response of peptide-conjugated Cur-loaded albumin-NPs against the BC cells [ 101 ]. The P-HAS/Cur-NPs also exhibited higher cytotoxicity against the MCF7 cells compared to the unfunctionalized HAS/Cur-NPs and the free Cur. As expected, the cell uptake study (florescent microscopy and flow cytometry) also validated superior ability of PDL-1 conjugated albumin-NPs (P-HAS/Cur-NPs) to internalize into BC cells (higher fluorescence) compared to the unfunctionalized NPs (HAS/Cur-NPs) and free Cur. These results evidenced that PDL-1 decoration on the surface of Cur-loaded albumin-NPs can be a promising adaption to maximize the selective targetability and anticancer efficacy against the BC [ 101 ].

On the other hand, Thadakapally and research group [ 102 ] proposed the PEGylation of Cur-loaded albumin-NPs to prolong their plasma circulation time, enhance the passive accumulation into BC cells and to reduce the hepatic clearance. The anticancer efficacy of prepared NPs (PEG-Cur-albumin-NPs) was tested against the BC cells (MD-MB-231). A biphasic release pattern was exhibited by the developed PEG-Cur-albumin-NPs with initial burst release followed by the sustained release over 35 days. The cell uptake study indicated a significantly higher uptake efficiency of PEGylated NPs into MD-MB-231 cells compared to the unPEGylated albumin-Cur-NPs and the free Cur [ 102 ]. In comparison with unfunctionalized albumin-Cur-NPs and the free Cur, lower uptake of PEG-albumin-Cur-NPs by the liver and kupffer cells indicated a marked stealthing effect of PEGylation. These findings indicated that PEGylation of Cur-loaded albumin-NPs could be a promising adaption to improve the plasma circulation time, preferential accumulation into BC cells (via EPR effect) and enhanced anticancer efficacy against the BC [ 102 ].

Recently, a newest design called “multifunctionalized NPs” have been developed by the Hiremath et al. [ 103 ] for the co-delivery of Cur and 5-flurouracil (5-FU) and evaluated against the BC cells (MCF7). Briefly, the Cur/5-FU co-loaded iron-oxide NPs were synthesized and decorated with albumin (to confer them with stealthing effect and to prolong the plasma half-life) and citrate (to render them a selective targeting feature for receptor-mediated endocytosis into BC cells). Furthermore, FA was conjugated to albumin to maximize the selective targetability to BC cells/tissues. The fabricated multifunctionalized NPs exhibited ultrafine particle size (10–15 nm), good anionic zeta potential (−49 mV), good colloidal stability, and a sustained release profile for both drugs (Cur and 5-FU). The cell uptake study indicated a superior internalization of multifunctionalized NPs (FA-albumin-Cur/5-FU-NPs) into the BC cells compared to the unfunctionalized NPs (Cur/5-FU-NPs) and the free Cur. The FA-albumin-Cur/5-FU-NPs also produced highest cytotoxicity in the BC cells compared to the control groups. These results indicated that multifunctionalization could be a successful evolving strategy to maximize the plasma circulation time, cell uptake, biodistribution to TME, specific targetability, and anticancer efficacy against the BC [ 103 ].

Polymeric nanoparticles (NPs)

Polymeric NPs are among the most extensively studied nanodelivery systems due to their intrinsic features including the particle size, morphology, entrapment efficiency, biodegradability, biocompatibility, colloidal stability, and good flexibility of functionalization [ 104 , 105 , 106 , 107 , 108 , 109 , 110 ]. Depending upon their architecture, polymeric NPs can be classified into (a) nanosphere and (b) nanocapsules [ 111 ]. The nanospheres are polymeric NPs in which drug(s) are encapsulated within the polymeric solid matrix or adsorb on the surfaces; however, in the nanocapsules drug(s) are mainly encapsulated within the inner core which is surround by a solid matrix of polymer. The properties of polymeric NPs largely depend upon the type of polymer used to construct them [ 111 , 112 , 113 ]. A wide variety of polymers such as natural, synthetic, and semi-synthetic polymers have been employed to fabricate the polymeric NPs [ 112 , 113 , 114 , 115 ].

Having considered their intrinsic physicochemical features and diversity of functionalization, Pawar et al. [ 116 ] developed PLGA-NPs for co-delivery of Cur and DTX. The developed Cur/DTX-PLGA-NPs were investigated for cytotoxicity, cell uptake, and pharmacokinetic profile (in male Wistar rats). The Cur/DTX-PLGA-NPs exhibited ultrafine particle size (219 nm), narrow PDI, anionic zeta potential (−14 mV), good encapsulation efficiency (⁓67%), and biphasic release kinetics. The cell uptake experiment indicated a significantly higher uptake of Cur/DTX-PLGA-NPs into BC cells (MCF7 cells) compared to the free Cur. The pharmacokinetic study revealed that Cur/DTX-PLGA-NPs displayed fivefold higher AUC in comparison with free drug. These results concluded that co-encapsulation of Cur and DTX into PLGA-NPs could improve their pharmacokinetic profile and anticancer efficacy against the BC [ 116 ].

For maximizing the cell uptake efficiency of PLGA-NPs into BC cells (MCF7), Sampath et al. [ 117 ] developed PLGA-NPs and functionalized them with a variety of surface functionalizing agents such as PEG, TPGS, chitosan and dextran. The functionalized Cur-PLGA-NPs exhibited ultra-fine particle size (< 250 nm), good loading capacity (> 90%), smooth spherical morphology, and good storage stability. The comparative analysis between different nanoformulations indicated that Cur-PLGA-NPs functionalized with TPGS exhibited highest cytotoxicity via the ROS-induced apoptosis in BC cells. Similarly, the cell uptake experiment also demonstrated highest internalization of TPGS-Cur-PLGA-NPs into MCF-7 cells compared to the Cur-PLGA-NPs functionalized with other moieties (PEG, chitosan, and dextran) and the free Cur [ 117 ].

One of the major obstacles for PLGA-NPs as a drug delivery device is the recognition by the RES [ 118 ]. Upon recognizing them as foreign materials, RES engulf the PLGA-NPs with subsequent metabolism and excretion from the body via the lymphatic drainage. To avoid RES-mediated recognition and clearance of Cur-PLGA-NPs, various surface functionalization strategies have been adapted. One of these approaches which gained exceptional recognition is the formation of hydrophilic layer around the exterior surface of polymeric NPs using a hydrophilic moiety (e.g., PEG) [ 119 , 120 ]. This technique has been widely employed for improving the plasma circulation time, pharmacokinetic profile, biodistribution, cell uptake efficiency (EPR effect) and anticancer efficacy of Cur-PLGA-NPs against the BC [ 117 , 121 ]. The PEG-Cur-PLGA-NPs upregulate ROS-induced apoptosis, alleviate angiogenesis (via inhibition of VEGF expression), reduced proliferative property (cyclin-D1) and great anti-metastatic potential (via declining MMP-9 expression). A strong anti-proliferative efficacy of PEG-Cur-PLGA-NPs was attributed to arresting of cell cycle at G2/M phase in MCF7 cells. The anticancer efficacy of PEG-Cur-PLGA-NPs was also validated in tumor bearing animals which demonstrated an improved bioavailability, an extended plasma half-life, high biodistribution into tumor tissues, and superior anticancer efficacy [ 121 , 122 ].

Another method of functionalization of Cur-PLGA-NPs was proposed by Palange and co-researchers [ 123 ] by coating the surface of NPs with lipids to confer them hydrophobicity for better permeability and uptake into MDA-MB-231 cells. One of the promising causes of high fatality rate in BC patients is high metastatic rate and invasion of BC cells into other organs. BC cells travel from primary tumor sites to other body regions through vascular or lymphatic systems [ 124 ]. These circulating tumor cells (CTCs) are capable of translocating from the primary sites (BC tissues) to other body organs and can stay in blood for prolonged period and may affect other normal tissues in a way similar as that leukocyte recruitment [ 125 ]. This also results in initiation of inflammation at the site where metastasis occurs [ 126 , 127 ]. Though, Cur exhibits a potent anti-inflammatory, anti-proliferative, and CTCs migration inhibition efficacy [ 128 , 129 ]; however, poor bioavailability, low water solubility, and minimal adsorption of Cur [ 130 , 131 , 132 ] reduce its anticancer viability. To avoid these problems, Palange and coworkers developed a long-circulating lipid-coated Cur-PLGA-NPs for efficient targeting to CTCs (MDA-MB-231) and inflamed endothelial cells (HUVECs) (TNF-α stimulated human umbilical vein endothelial cells) [ 123 ]. The developed NPs consisted of a polymeric core (made up of PLGA) containing Cur and an outer covering containing a mixture of lipids and PEG to stabilize the Cur-PLGA-NPs as well as to extend the plasma half-life. The lipid-coated Cur-PLGA-NPs showed a dose- and time-dependent cytotoxicity in HUVECs and MDA-MB-231 cells which was significantly higher (lower IC 50 ) than the free Cur. Moreover, the adhesion of CTCs to endothelial cells (a hallmark of cancer metastasis) was significantly decreased after the treatment with lipid-Cur-PLGA-NPs compared to the unfunctionalized Cur-PLGA-NPs and the free Cur. These findings demonstrate that lipid coating could be a promising adaption in the design of polymeric NPs for improving their penetrability and anticancer efficacy against the BC [ 123 ].

For selective targeting and receptor-mediator endocytosis, Zheng and co-workers [ 133 ] functionalized the exterior surface of Cur-PLGA-NPs with transferrin to recognize and target the transferrin receptors overexpressed on the surface of BC cells. The transferrin-conjugated Cur-PLGA-NPs (T-Cur-PLGA-NPs) showed a significantly higher cell uptake into BC cells (MDA-MB-231) compared to unfunctionalized NPs (Cur-PLGA-NPs) which was attributed to transferrin receptors-mediated endocytosis. The superior uptake of T-Cur-PLGA-NPs was responsible for higher cytotoxicity into BC cells compared to the Cur-PLGA-NPs and the free Cur [ 133 ]. Likewise, Yallapu et al. [ 134 ] prepared the Cur-PLGA-NPs and tagged with transferrin or anti-TAG-72 monoclonal antibody (Mab) for selective targeting of BC cells. The optimized T-Cur-PLGA-NPs exhibited ultrafine particle size (< 100 nm), narrow PDI, smooth spherical morphology, good encapsulation efficiency, and sustained release profile (up to 25 days). The cell uptake study depicted that T-Cur-PLGA-NPs displayed a superior cell uptake efficiency and anti-proliferative efficacy and lower colony formation in BC cells (Fig. 7 ) compared to the Cur-PLGA-NPs and free CUR. The superior anti-proliferative efficacy of T-Cur-PLGA-NPs was attributed to enhanced ROS-mediated apoptosis in the BC cells [ 134 ]. These results were later confirmed by another research group [ 135 ].

Superior anti-proliferative efficacy of T-Cur-PLGA-NPs (nano-CUR) in ovarian cells (A2780CP) ( A & B ) and BC cells (MDA-MB-231) ( C & D ) compared to free Cur (20 µM). Adapted from [ 134 ]

Like other biochemical targets, annexin A2 (AnxA2) is also well-studied biochemical target that overexpressed on the surface of BC cells and its expression is very high in aggressive BC cells having great metastatic potential. Ranjan and colleagues were granted a US-patent on the surface functionalization of Cur-loaded PLGA-NPs with AnxA2 antibody. The in vitro testing of AnxA2-Cur-PLGA-NPs against the BC cells (MDA-MB-231) showed a significant increase in the cell uptake efficiency, reduction in cell proliferation, migration, and invasion, metastasis, and growth [ 136 ]. The prepared Cur-PLGA-NPs were optimized using the central composite design (Design Expert ® ) for particle size (⁓145 nm), encapsulation efficiency (⁓90%), release profile (⁓60% drug release in 24 h), percent yield (⁓90%), morphology (smooth spherical), and storage condition (stable at 4 °C). These results concluded that AnxA2-functionalization could be a promising adaptation in the design of polymeric NPs for selective targeting of BC cells/tissues [ 136 ]. These results were later confirmed by Mukerjee et al. [ 137 ].

Another innovative design of Cur-nanomedicines (hybrid Nanocurcumin) which was granted US-patent in 2015 was proposed by Ranjan and colleagues in which they fabricated Cur-loaded PLGA-NPs and encased them into lipid-envelope [ 138 ]. The hybrid Nanocurcumin (liposoma-PLGA-curcumin) containing the Cur within the polymeric shell was characterized for particle size, encapsulation efficiency, morphology, release kinetics, cell uptake efficiency, and anticancer efficacy against different cancer types including the BC. The hybrid Nanocurcumin showed significant improvement in bioavailability, cell uptake efficiency, cytotoxicity (lower IC 50 ), and potent anticancer efficacy against the BC cells compared to the plain Cur-loaded liposomes and free Cur. Another interesting fact about the hybrid Nanocurcumin was its superior safety profile against the QT-prolongation which is one of the most promising side effects caused by the Cur and other conventional Cur-nanosystems [ 138 ].

Polymeric micelles

Polymeric micelles (PMs) have been extensively deployed as targeted drug delivery devices for a wide variety of therapeutics particularly for poorly water-soluble drugs [ 139 ]. Multi-drug resistance (MDR) is one of the potential hurdles for effective treatment of various types of cancers including the BC [ 140 , 141 ]; however, to overcome this obstacle, a combination therapy has been adapted for multi-target action. Ma et al. [ 142 ] developed hyaluronic acid-vitamin E succinate copolymer-based PMs for simultaneous delivery of Cur and Dox. The developed Dox-Cur-PMs exhibited nanoscaled dimension (⁓223 nm), anionic surface charge (-10 mV), smooth spherical morphology, and high encapsulation efficiency (Cur ⁓72% and Dox ⁓95%). The release study demonstrated that both encapsulated drugs exhibited more prominent and rapid release in an acidic environment (pH 4.5–5.5) compared to the physiologic pH (pH 7.0) which indicate their potential for tumor-specific delivery. On the other hand, in vivo study showed that 4T1 cells bearing mice treated with Dox-Cur-PMs demonstrated greater suppression in the tumor volume compared to other treatment groups. The tumor bearing animals treated with Dox-Cur-PMs displayed a significant suppression of tumor growth rate (⁓55%) compared to the free drugs (Dox + Cur) (⁓28%). The compatibility evaluation of Dox-Cur-PMs evidenced no signs of cardiotoxicity and hepatotoxicity in contrast to the free Dox which induced noticeable cardiotoxicity and hepatotoxicity in the test animals (Fig. 8 ). These results demonstrate that PMs are promising nanocarriers for co-encapsulation of two chemotherapeutics to improve the anticancer efficacy and reduce the cardiotoxicity and hepatotoxicity associated with Dox [ 142 ].

Anticancer efficacy of DOX/CUR co-loaded PMs in 4T1 xenograft mouse model. A ) relative body weight, B ) tumor volume, C ) representative photographs and D ) average weight of excised tumor harvested from sacrificed mice at the end of the experiment, E ) serum levels of creatine kinase MB (CKMB), F ) aspartate transaminase (AST), G )creatine kinase (CK), and H ) lactate dehydrogenase (LDH) after intravenous administration of tested formulations in 4T1-bearing mice. Adapted from [ 142 ]

Like other delivery vehicles, ample opportunities for diverse functionalizations are implantable in the design of PMs. Hosseini et al. [ 143 ] developed the Cur-loaded PMs for the treatment of BC. To optimize the selective targeting into BC cells, the developed Cur-PMs were functionalized with anti-cyclin-D1 antibody. The cyclin-D1 is a mutagenic gene overexpressed on the surface of BC cells and is responsible for initiation of G1 phase in the cell cycle to promote proliferation of BC cells [ 144 , 145 , 146 ]. In addition, cyclin-D1 promotes cell migration, metastasis and increase tumor invasion [ 144 , 145 ]. The developed anti-cyclin-D1-Cur-PMs (SinaCurcumin ® ) were evaluated for the physicochemical properties, biopharmaceutical properties and therapeutic efficacy against the BC cells (MCF-7) [ 143 ]. Results depicted that nanoencapsulation of Cur into PMs resulted in a significant increase in its aqueous solubility and oral bioavailability. The cell cytotoxicity study revealed a pronounced suppression (⁓84%) in proliferation of BC cells treated with SinaCurcumin ® compared to the different chemotherapeutic agents such as 5-FU (⁓75%), adriamycin (⁓70%), and cyclophosphamide (⁓63%) [ 143 ]. These results suggested that SinaCurcumin® is a more potent anticancer agent compared to the most used anticancer agents such as cyclophosphamide, adriamycin and 5-fluorouracil. It was also established that cyclin-D1 expression was significantly declined in MCF-7 cells treated with SinaCurcumin ® , demonstrating the downregulation of cyclin-D1 expression on the BC cells. These results concluded that conjugation of anti-cyclin-D1 on the surface of Cur-PMs could be a promising adaption in the design of PMs for the treatment of BC [ 143 ].

Another innovative method of functionalization of Cur-PMs was reported by the Xie et al. [ 147 ] in which dual role of methotrexate (MTX) was proposed. Besides demonstrating a fair anticancer efficacy, MTX possess moderate targeting efficiency (as targeting ligand) for tumor cells having overexpressed FA-receptors due to its structural similarity with FA. In this study, MTX was tagged on the surface of Cur-PM via imine linkage (acid-sensitive linkage which hydrolyze in an acidic environment of TME). The developed Cur-loaded MTX-imine-PMs (MTX-imine-M-Cur) were evaluated for cytotoxicity, cell uptake efficiency, and in vivo anticancer efficacy against the HeLa tumor-bearing BALB/c nude mice. The fabricated MTX-imine-M-Cur exhibited ultrafine particle size (20–30 nm), anionic surface charge, good encapsulation efficiency, smooth spherical morphology, and good colloidal stability. As expected, Cur and MTX release was pronounced at acidic pH (5.0) compared to physiological pH (7.4) which evidence the site-specific release behavior of MTX-imine-M-Cur. The cell culture study revealed a significantly higher uptake efficiency and greater cytotoxicity in BC cells (MCF-7) compared to the control groups. The tumor-bearing animals treated with an intravenous injection of MTX-imine-M-Cur (equivalent to 8 mg/kg) displayed a significantly higher anticancer efficacy in terms of smallest tumor volume and insignificant variability in the body weight compared to the control groups (0.9% NaCl, Cur, MTX/Cur, M-Cur, and MTX-amide-M-Cur) (Fig. 9 ). These results evidenced the significance of imine linkage for the site-specific release of chemotherapeutic payload as well as a dual role of MTX as chemotherapeutic agent and targeting ligand for FA-receptors overexpressed on the surface of BC cells [ 147 ].

Anticancer efficacy of MTX-imine-M-Cur in HeLa tumor-bearing nude mice after intravenous injection compared to 0.9% NaCl, free Cur, Cur/MTX, M-Cur, and MTX-amide-M-Cur at an equivalent dose of Cur (8 mg/kg). A ) tumor volume, B ) body weight, C ) average weight of excised tumor, and D ) H&E histologic images of tumors resected from experimental animals. Adapted from [ 147 ]

A Chinese patent has also been granted on Cur-loaded PMs (triphenylphosphine-chitosan-stearic acid graft) prepared by ultrasonication technique and evaluated for the management of BC (MCF-7 cells) [ 148 ]. The developed PMs exhibited ultra-fine particle size, high encapsulation efficiency, good percent yield, and protection of Cur against the photo-oxidation. The incubation of Cur-loaded PMs with the MCF-7 cells displayed a significantly higher cell uptake efficiency, cytotoxicity, and inhibited migration and invasion compared to the free Cur [ 148 ].

Niosomes are innovatively designed nanovesicles that have been extensively developed for targeted delivery of chemotherapeutics [ 149 ]. These vesicular delivery systems can overcome different problems associated with bioactive(s) such as poor aqueous solubility, low oral bioavailability, degradation, and low endocytosis into various cells including the BC cells [ 150 ]. These nanovesicles are formed by the combination of cholesterol and non-ionic surfactants.

Owing to their unique composition and structural simulation with the biological membrane, niosomes have been extensively deployed as a drug delivery carrier for a wide variety of drugs to improve their physicochemical properties and biomedical efficacy [ 151 ]. One of the promising features of the niosomes is ability of simultaneous delivery of multiple payloads with different physicochemical properties (such as co-delivery of hydrophilic and hydrophobic drugs) [ 152 ]. Naderinezhad et al. [ 152 ] designed a hybrid model of niosomes by combining the liposomes and niosomes (LipoNiosome) together for co-delivery of Dox and Cur and evaluated for anticancer efficacy against the various cancer cells. The engineered Cur-Dox-LipoNiosomes exhibited an ultra-small particle size (⁓40 nm), high encapsulation efficiency (⁓80%), pH-responsive release of entrapped drugs (predominant release at acidic pH compared to the physiologic pH), and sustained release kinetics. The cell uptake study suggested that LipoNiosome exhibited an excellent cell internalization ability with a specific uptake and higher cytotoxicity in all cancer cells compared to the free drugs [ 152 ].

Aiming to extend the plasma circulation time and improving the passive transfection into BC cells (MCF-7), Alemi and co-researchers [ 153 ] developed PEG-functionalized Cur/PTX co-loaded niosomes. The PEGylated niosomes (PEG-Cur/PTX-niosomes) exhibited an ultrafine vesicle size (~ 90 nm), excellent encapsulation efficiency (~ 100%), good zeta potential (+ 15 mV), and satisfactory colloidal stability. The PEG-Cur/PTX-niosomes showed the pH-responsive release with predominant release at an acidic pH and relatively a slower release at the physiologic pH which indicate their ability to specifically release the therapeutic cargo at TME. The cell uptake study demonstrated a specific uptake of PEG-Cur/PTX-niosomes into MCF-7 cells (BC cells) compared to the MCF-10A (normal epithelial breast cells). The cytotoxicity study suggested a significantly higher cytotoxicity in MCF-7 cells treated with PEG-Cur/PTX-niosomes compared to the unPEGylated Cur/PTX-niosomes and the free drugs [ 153 ].

Another novel functionalization strategy was proposed by Akbarzadeh et al. [ 154 ] in which they designed Cur-loaded niosomes shelled with calcium alginate (CA) and evaluated for the treatment of BC. Due to unique properties of CA, these delivery systems were expected to have better biopharmaceutical and therapeutic potential. The anticancer efficacy of CA-shelled Cur-niosomes was evaluated against various BC cells (SKBR3 and MDA-MB231) and results were compared with the normal breast cells (MCF-10A cells). The fabricated CA-Cur-niosomes exhibited nano-scaled size, good encapsulation efficiency, and good colloidal stability (up to 1 month at 4 °C). The release study depicted a pH-responsive release behavior of CA-Cur-niosomes with predominant release at acidic pH and relatively lower release at physiological pH [ 154 ]. In addition, the developed CA-Cur-niosomes showed good biocompatibility against the MCF10A cells but higher cytotoxicity against SKBR3 and MDA-MB231 cells. The cell uptake study indicated a specific endocytosis of CA-Cur-niosomes into BC cells compared to relatively lower internalization into MCF-10A cells. The biochemical analysis revealed a significant suppression in the expression of cancerous genes (e.g., Bcl2, cyclin E and cyclin D) and higher expression of pro-apoptotic genes (e.g., caspase-3, Bax, P53 and caspase-9) in BC cells treated with CA-Cur-niosomes compared to unfunctionalized Cur-niosomes and the free Cur [ 154 ].

Multifunctionalization is an emerging adaptation in the nanotechnology to achieve versatile benefits such as prolonging of plasma circulation time (via PEGylation), stimuli-responsive behavior (e.g., pH, temperature, light, etc.), specific targetability (via targeting ligand), simultaneous delivery of multi-therapeutics (e.g., genes, DNA, RNA, chemotherapeutics, proteins, etc.), and many more [ 155 , 156 , 157 ]. Keeping in view of the diversity of this relatively newer strategy of functionalization, Honarvari et al. [ 158 ] designed FA-decorated PEGylated Cur-niosomes (PEG-FA@Nio-CUR) aiming to prolong the plasma half-life, to achieve specific targetability, and to augment cell uptake efficiency via FA-receptor mediated endocytosis into BC cells. The developed PEG-FA@Nio-Cur exhibited nano-scaled vesicle size (⁓190 nm), narrow PDI, good zeta potential (−28 mV), good colloidal stability and high encapsulation efficiency (> 90%). The multifunctionalized PEG-FA@Nio-CUR also displayed a pH-responsive release behavior with predominantly higher release at acidic pH (pH 5.4) and relatively lower release at physiologic pH (pH 7.4) which indicates their target-specific release into TME. The cytotoxicity study showed highest cell viability in MCF-10A cells (normal breast epithelial cells) treated with PEG-FA@Nio-Cur which indicated good cytocompatability compared to unfunctionalized Nio-Cur and free Cur. The cell viability against the BC cells (MCF7 and 4T1) showed highest cytotoxicity (lowest IC 50 ) of PEG-FA@Nio-Cur compared to the controls. Furthermore, a significant upregulation in the expression of Bax and p53 genes with marked downregulation in the expression of Bcl-2 were observed in MCF-7 and 4T1 cells treated with PEG-FA@Nio-Cur compared to other treatments (Fig. 10 ) [ 158 ]. The cell uptake study also indicated the dominance of PEG-FA@Nio-Cur for endocytosis into BC cells compared to unfunctionalized Nio-Cur and free Cur. These results concluded that multifunctionalization is a promising adaptation in the design of niosomes to improve selective targetability and anticancer efficacy against the BC [ 158 ]. Likewise, Lin et al. [ 159 ] developed FA-PEG-Cur-NLCs and evaluated for physicochemical characterization, pH-responsive release, stability, cytotoxicity, biocompatibility, cell uptake efficiency, specific internalization, and anti-tumor efficacy against the BC. The fabricated FA-PEG-Cur-NLCs exhibited ultrafine particle size (127 nm), cationic zeta potential (+ 13 mV), and high encapsulation efficiency (⁓85%). The drug release study depicted a relatively slower release of Cur from FA-PEG-Cur-NLCs compared to unfunctionalized NPs which indicated their sustained release behavior. The cytotoxicity study displayed a significantly higher (3.5-fold) toxicity in MCF-7 cells treated with FA-PEG-Cur-NLCs compared to unfunctionalized Cur-NLCs and the free Cur. The superior antitumor efficacy of FA-PEG-Cur-NLCs was also validated in animals (Balb/c nude mice) in terms of smallest tumor volume (312 mm 3 ) compared to the control groups following the IV administration. These results demonstrated that functionalization of NPs is a promising strategy to augment anticancer efficacy of Cur [ 159 ]. The anti-tumorigenesis efficacy of FA-functionalized niosomes has also been recently validated by Rezaei et al. [ 160 ] for the treatment of BC. Results suggested a significantly higher cell uptake, cytotoxicity, and apoptosis in MCF-7 and 4T1 cells treated with FA-functionalized niosomes compared to unfunctionalized niosomes. Similar findings have also been reported by other studies [ 161 , 162 , 163 , 164 ].

Upregulated expression of Bax ( A, D ) and p53 genes ( C, F ) and downregulation in the expression of Bcl2 ( B, E ) in MCF7 and 4T1 cells treated with PEG-FA@Nio-Cur compared to vehicle (Nio), free Cur, and unfunctionalized Nio-Cur. Adapted from [ 158 ]

Dendrimers are highly ordered, branched, and high molecular weight polymeric macromolecules which are formed of many functional groups. Due to their unique compact architecture, low polydispersity, and intrinsic physicochemical properties, dendrimers have gained remarkable recognition as drug delivery devices for a wide variety of drugs, proteins, genes, and RNAs. Unlike the traditional polymers, good aqueous solubility, polyvalency, biocompatibility, and precise molecular weight of dendrimers make them highly suitable drug delivery device for various biological applications [ 87 , 165 , 166 , 167 ].

Keeping in view of their unique physicochemical features, Debnath and co-researchers [ 168 ] developed Cur-loaded dendrimers for improving the physicochemical properties and anticancer efficacy of Cur against the BC. A significant improvement in aqueous solubility, bioavailability, transfection efficiency, and cytotoxicity against the BC cells (SKBr3 and BT549 cell lines) was evident compared to the free Cur. The potent anticancer efficacy against the BC cells was attributed to an induction of apoptotic cell death via the activation of caspase-3 [ 168 ].

Aiming to confer an active targeting feature to the dendrimers, Shi et al. [ 169 ] developed Cur-loaded PAMAM dendrimers and functionalized with FA. The fabricated FA-Cur-dendrimers were evaluated for various physicochemical properties, pH-responsive release, and specific cell uptake efficiency into BC cells. A significant improvement in the aqueous solubility of Cur was evident from FA-Cur-dendrimers. The release study depicted a pH-responsive release of Cur with predominant release at an acidic pH which was attributed to presence of acid-labile phenolic ester group [ 169 ]. The cell uptake study showed a significant upregulation in the transfection efficiency of Cur into the BC cells from FA-Cur-dendrimers compared to unfunctionalized Cur-dendrimers and free Cur, and it was expected to be due to FA-receptor mediated endocytosis. These results evidenced that functionalization is a promising adaptation in the design of dendrimers to maximize the specific targetability and anticancer efficacy of Cur-nanomedicines against the BC [ 169 ].

Other nanosystems

A variety of other nanosystems have also been engineered for improving the pharmacokinetic profile, cell uptake efficiency, specific targetability, and cytotoxicity of Cur against the BC. Keeping in view of unique physicochemical properties, inorganic architecture, morphological features, surface characteristics, ultrafine particle size, and high stability, Ding et al. [ 170 ] developed TiO 2 -NPs for the co-delivery of Salvianolic acid B (Sal B) and Cur to achieve synergistic anticancer efficacy against the BC. Sal B is a traditional herb extensively used in the China as a circulation enhancing agent. In addition to its potent antioxidant, anti-inflammatory, and anti-coagulation effect, Sal B possess a moderate anticancer efficacy against a variety of cancers including the BC. Therefore, Ding and co-workers proposed the co-encapsulation of Cur and Sal B into the TiO 2 -NPs for a synergistic anticancer efficacy against the BC. Furthermore, to prolong the plasma circulation time and to achieve the specific targeting into the BC cells, the fabricated NPs were functionalized with PEG and FA (as targeting ligand). The fabricated FA-PEG-Cur/SalB-TiO 2 -NPs exhibited an ultrafine particle size (15–26 nm), narrow PDI, and good loading capacity. The cell cytotoxicity experiment showed a dose-dependent and significantly higher cytotoxicity in MCF-7 and MDA-MB-231 cells treated with FA-PEG-Cur/SalB-TiO 2 -NPs compared to unfunctionalized Cur/SalB-TiO 2 -NPs and free drugs (Cur and Sal B). The superior anti-proliferative efficacy of FA-PEG-Cur/SalB-TiO 2 -NPs was attributed to their highest uptake into both BC cells (MCF-7 and MDA-MB-231) compared to unfunctionalized Cur/SalB-TiO 2 -NPs and free drugs. The anticancer efficacy of FA-PEG-Cur/SalB-TiO 2 -NPs was also tested in vivo . The experimental animals (MDA-MB-231 bearing nude Balb/c mice) were injected (into tail vein) the test formulations and evaluated for biodistribution of Cur and Sal B into various tissues including the tumor tissues. The resulting fluorescent images clearly evidenced a time-mannered biodistribution of FA-PEG-TiO 2 -NPs into the tumor and liver tissues with subsequent excretion from the body (Fig. 11 ) [ 170 ]. Initially at 4 hours post-injection, the biodistribution was mainly observed in the liver tissues which was subsided at 12 h and become equivalent to the tumor tissues. After 24 h, the biodistribution was highest in the tumor tissues with subsequent decrease in 48 h which indicate a successful excretion of drugs from the body. These results evident the biopharmaceutical and therapeutic feasibility of inorganic NPs (TiO 2 -NPs) for the successful co-delivery of Cur and Sal B for the treatment of the BC [ 170 ].

In vivo biodistribution of FA–PEG–TiO 2 –NPs into tumor tissues and other body organs compared to unfunctionalized TiO 2 -NPs after injection into tail vein ( A ), ex vivo fluorescence imaging of dissected organs ( B ), and fluorescence intensity in different organs and tumor at 4 ( C ), 12 ( D ), 24 ( E ) and 48 hours ( F ). Adapted from [ 170 ]

An innovative design of magnetic NPs (Fe 2 O 3 -NPs) was also granted US-patent in 2013 in which β-cyclodextrin (β-CD)-citrate coated Fe 2 O 3 -NPs were prepared and loaded with Cur. The FTIR, XRD, and 1 H NMR data indicated the formation of an inclusion complex of Cur into β-CD which resulted in a sustained release of Cur from the inclusion complex [ 171 ]. The physicochemical characterization of synthesized Cur-loaded β-CD-citrated coated Fe 2 O 3 -NPs showed nanoscaled dimension (<10 nm), high anionic surface charge (20–35 mV), good encapsulation efficiency, and good aqueous solubility (⁓60 mg/mL). Authors of this work demonstrated that these magnetic NPs can be used as a contrast agent as well as therapeutic agent against the BC which make them a successful theranostics for early detection and treatment of BC [ 171 ]. A summary of different types of functionalization strategies (e.g., PEGylation, targeting ligand conjugation, stimuli-responsive release, multifunctionalization, etc.) adapted for the improvement of pharmacokinetic profile, selective targeting, and antitumor efficacy of Cur against the BC has been presented in the Table 2 .

Summary and conclusions

Despite exhibiting the promising anticancer efficacy against the BC, clinical translation of curcumin (Cur) is restricted due to chemical instability (e.g., photodegradation), hydrophobicity, poor absorption, low bioavailability, short plasma half-life, and lower distribution to different body tissues. To mitigate these shortcomings, diverse types of nanocarriers such as liposomes, SLNs, polymeric micelles, polymeric nanoparticles, niosomes, dendrimers, and inorganic NPs have been deployed as delivery devices for the Cur. The implication of nanotechnology has significantly augmented the physicochemical properties, bioavailability, plasma half-life, transfection efficiency, cell uptake, and anticancer efficacy against the BC. However, majority of the Cur-nanomedicines are still facing grander challenges due to recognition by the reticuloendothelial system, non-specific accumulation into various body tissues, poor penetration and accumulation in the TME, and multidrug resistance due to overexpressed P-glycoproteins in the tumor cells which ultimately hampers the clinical significance of Cur-nanomedicines.

In the recent decades, plentiful newest developments have been adapted to overcome challenges associated with Cur-nanomedicines and to augment their anticancer efficacy against the BC. Among these developments, the functionalization of Cur-nanomedicines has gained remarkable recognition. Many dynamic functionalization strategies have been adapted in the design of Cur-nanomedicines including the PEGylation, conjugation of targeting ligand(s), pH-responsiveness, co-delivery of multiple therapeutics, and multifunctionalization. The critical analysis of available literature revealed that PEGylation (physical decoration or chemical conjugation of PEG on NPs surfaces) can successfully extends the plasma half-life by repressing the recognition and subsequent metabolism of Cur-nanomedicines by the RES. Plethora of studies have reported that PEGylation significantly improves the passive uptake and internalization of Cur-nanocarriers into the BC cells with only minimal-to-no uptake into the normal epithelial breast cells. To maximize the specific uptake into the BC cells/tissues as well as to avert non-target accumulation, many researchers have adapted active targeting strategy in which Cur-nanomedicines were conjugated with diverse targeting ligands (e.g., FA, transferrin, PDL-1, hyaluronic acid, monoclonal antibody, affibody, etc.). The selection of a specific targeting ligand(s) to conjugate on the exterior of the Cur-nanomedicines mainly depends upon the type of biochemical target (e.g., FA-, transferrin- and/or CD44-receptors, or other genes and proteins) overexpressed on the surface of BC cells. The Cur-nanomedicines having capped with targeting ligand(s) resulted in a significant increase in specific uptake and internalization into BC cells which ultimately results in a significant decrease in IC 50, and enhanced cytotoxicity compared to unfunctionalized Cur-nanomedicines and the free drug(s). On the other hand, the Cur-nanomedicines exhibiting a pH-responsive behavior expressed predominant release at the acid pH (4.5–5.0) compared to the physiologic pH, which indicates their site-specific release at TME. Besides these functionalization, multifunctionalization is the most recent adaptation in the design of Cur-nanomedicines in which nanocarrier’ architecture is functionalized with multiple functionalities such as PEGylation + targeting ligand + pH-responsiveness + combo delivery of multiple therapeutics. These multifunctional Cur-nanomedicines displayed an exceptional potency and powerful anticancer efficacy against the BC while ensuring an immense safety against normal healthy cells. The convincing evidences compiled in this review article have demonstrated that diverse types of functionalizations can be adapted while designing a novel Cur-nanomedicine to improve the physicochemical properties, pharmacokinetic profile, specific cell uptake, and anticancer efficacy of Cur against the BC. Despite all these evolutions, several aspects of Cur-nanomedicines are still debatable such as poor reproducibility during the manufacturing, poor in vitro–in vivo correlation, unexplained nanotoxicity due to unwanted interaction of nanomaterials with the biological tissues, lacking the establishment of acute and chronic toxicities, and absence of specific international guidelines for the manufacturing, administration, and the safety of Cur-nanomedicines for the treatment of BC.

Availability of data and materials

Not applicable.

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Acknowledgements

Authors would like to greatly acknowledge Beijing-Tianjin-Hebei Basic Research Cooperation, Key Laboratory of Molecular Pathology and Early Diagnosis of Tumor in Hebei Province, and University of Sharjah, United Arab Emirates for all support to accomplish this review project.

This work was supported by Beijing-Tianjin-Hebei Basic Research Cooperation Special Project (2019) "Visualized Stem Cell Targeted Tumor Therapy Techniques for Precise Diagnosis and Treatment of Tumors" (No. H2019104018) and Key Laboratory of Molecular Pathology and Early Diagnosis of Tumor in Hebei Province.

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Jinku Zhang, Jirui Sun & Haizhi Qiao

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Department of Pharmaceutics and Pharmaceutical Technology, College of Pharmacy, University of Sharjah, 27272, Sharjah, United Arab Emirates

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JS, CL, and HQ performed a thorough literature survey, critically analyses the available data, and wrote the original draft. JZ conceptualized the methodology of this project and mentor and supervised throughout the writing, editing, and proofreading of the manuscript. ZH performed a final editing and proofreading to this manuscript as well as created and licensed original figures (Figs. 1 – 5 and graphical abstract) from Biorender.com and secured permission to reuse third-party materials (Figs. 6 – 11 ). All authors read and approved the final manuscript.

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Zhang, J., Sun, J., Li, C. et al. Functionalization of curcumin nanomedicines: a recent promising adaptation to maximize pharmacokinetic profile, specific cell internalization and anticancer efficacy against breast cancer. J Nanobiotechnol 21 , 106 (2023). https://doi.org/10.1186/s12951-023-01854-x

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Received : 24 January 2023

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DOI : https://doi.org/10.1186/s12951-023-01854-x

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Abstract : The Caenorhabditis elegans (C. elegans) connectome, with its relatively simple structure of approximately 300 neurons and several thousand synaptic connections, presents as the most immediately tractable target for whole-nervous system functional simulation. Despite this potential, research has predominantly focused on isolated neural circuits due to technical limitations such as the field of view of existing microscopes, activity indicator considerations, and the complexity of mapping interneuron connections. Existing whole-brain models of C. elegans often rely on assumptions due to inadequate biophysical data per neuron, limiting their accuracy and utility. We describe the development of a specialized lightsheet microscope capable of capturing isotropic high-resolution, functional activity across the entire worm during various behaviors, automated imaging by use of hybrid microfluidics, alongside novel neuron identification methods to precisely map neural identity, so as to generate a comprehensive dataset ideal for model training. Our goal is to create an accurate and interpretable whole-worm functional model of C. elegans, derived from real-world data under diverse behavioral conditions. The accuracy of the model will be validated by its ability to predict future neural activity based on initial states derived from actual worms, thereby offering a more accurate representation of the organism's neural dynamics. This effort not only advances our understanding of the C. elegans functional connectome but also sets a precedent for whole-brain modeling techniques applicable to broader neuroscientific studies.

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1. INTRODUCTION

2. overview of the potential of chatgpt usage in various fields, 3. technical limitations & ethical concerns, 4. flowcharts for efficient chatgpt usage, 5. conclusions & future directions, 6. aknowledgments, author contribution statement, chatgpt is a remarkable tool—for experts.

Amos Azaria is an associate professor at Ariel University, Israel. He received a B.A. degree in computer science from the Technion Institute of Technology, Haifa, Israel, in 2004, a PhD degree from Bar Ilan University, Ramat Gan, Israel in 2015 and was a post-doctoral fellow at CMU, Pittsburgh PA. After completing the Bachelor's degree he spent several years in the industry, some of which included working with Microsoft R\&D Haifa, Israel. Azaria has coauthored over 100 papers, has won the Victor Lesser distinguished dissertation award for 2015, and was a member of the winning team of the DARPA SMISC competition, 2015 on bot detection. His research interests include human-agent interaction, deep learning, human aided machine learning and natural language processing.

Dr. Rina Azoulay (B.A in Economics and Computer Science, Bar Ilan Univ. M.Sc. in Computer Science, Bar Ilan Univ., Ph.D. Computer Science, Bar Ilan Univ., 2001) is a senior lecturer in the Computer Science Department at the Jerusalem College of Technology, Israel. Her main research interests are protocols for reaching agreements, the application of machine learning methods, and Intelligent Tutoring Systems.

Dr. Shulamit Reches (B.A in Mathematics, Jerusalem college. M.Sc. in Mathematics and Computer Science, Bar Ilan Univ., Ph.D. Computer Science, Bar Ilan Univ., 2011) is a senior lecturer in the Mathematics Department at the Jerusalem College of Technology, Israel. Her main research interests are: Artificial Intelligent and Algebraic Combinatorics.

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Amos Azaria , Rina Azoulay , Shulamit Reches; ChatGPT is a Remarkable Tool—For Experts. Data Intelligence 2024; 6 (1): 240–296. doi: https://doi.org/10.1162/dint_a_00235

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This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains, including scientific writing, mathematics, education, programming, and healthcare. We explore the potential of ChatGPT to enhance productivity, streamline problem-solving processes, and improve writing style. Furthermore, we highlight the potential risks associated with excessive reliance on ChatGPT in these fields. These limitations encompass factors like incorrect and fictitious responses, inaccuracies in code, limited logical reasoning abilities, overconfidence, and critical ethical concerns of copyright and privacy violation. We outline areas and objectives where ChatGPT proves beneficial, applications where it should be used judiciously, and scenarios where its reliability may be limited. In light of observed limitations, and given that the tool's fundamental errors may pose a special challenge for non-experts, ChatGPT should be used with a strategic methodology. By drawing from comprehensive experimental studies, we offer methods and flowcharts for effectively using ChatGPT. Our recommendations emphasize iterative interaction with ChatGPT and independent verification of its outputs. Considering the importance of utilizing ChatGPT judiciously and with expertise, we recommend its usage for experts who are well-versed in the respective domains.

The field of artificial intelligence has rapidly evolved over the years, with natural language processing (NLP) models being one of the most promising areas of research. One of the notable developments in this realm is the advent of chatbots and conversational agents [ 2 ]. Owing to their capacity to mimic human responses to text inputs, they have surged in popularity. This rapid rise is greatly attributed to the advancement of large language modules (LLMs), which have significantly enhanced their performance. LLMs, also referred to as neural language models, are deep learning models that aim to generate human-like text. These models are trained on vast amounts of text data, enabling them to learn patterns, grammar, semantics, and context in a manner similar to human language acquisition. One such model that stands out is ChatGPT [ 117 ], an AI model with generative capabilities [ 44 ] crafted by OpenAI [ 19 ]. ChatGPT has demonstrated exceptional proficiency across diverse applications, and its latest version, ChatGPT4, exhibits amplified capabilities and is seen as a substantial stride towards achieving artificial general intelligence [ 20 ].

The most common ChatGPT uses, as described by the Business Community website ① , are: drafting emails to coworkers, writing a resume or cover letter, summarizing complex topics or long articles, getting answers to questions without traditional web search, writing songs, poetry, and screenplays based on existing content, writing and debugging computer code, translating content into multiple languages, writing essays on any topic, solving math problems, and finding new recipes based on a set of ingredients. In addition, people have also used ChatGPT for some applications that could potentially be risky, such as seeking medical or legal advice.

ChatGPT has proven to be a valuable tool to promote research. It may serve as a valuable source of inspiration, helping researchers generate ideas, improve textual expressions, find strategies for conducting research. By asking ChatGPT questions about relevant analyses or requesting research prompts, researchers can gain insights and guidance for their projects ② . Furthermore, ChatGPT proves advantageous for various textual operations, such as summarizing, rephrasing, rectifying errors, and translating text, tasks that are of critical importance during the course of any research study.

In the realm of education, ChatGPT can be leveraged to create personalized learning experiences for students. This is attainable by creating flexible, dynamic content customized to meet each student's individual requirements and by facilitating dialogues and providing responses to their questions. In creative writing, ChatGPT can assist authors in generating new ideas, enhancing their writing style, and providing valuable ideas. In programming, ChatGPT can assist developers in code generation, code debugging, suggesting solution concepts and designs, proposing algorithmic methods, and explaining them. In medicine, ChatGPT can be used to analyze medical records, assist in patient diagnosis and treatment, and provide empathetic conversations with the patients.

In summary, ChatGPT has proven itself as a potent instrument capable of enriching research, boosting efficiency, and refining writing styles across diverse disciplines. By responsibly and transparently employing ChatGPT, we can leverage the full potential of this technology to improve capabilities and promote innovation in different domains.

With ChatGPT's capabilities and limitations in mind, it becomes pertinent to delve into its applications among professional users, which is the primary focus of this paper. It's important to underscore that even in a professional context, deploying ChatGPT can pose significant challenges and risks. Consequently, addressing these challenges and ethical considerations [ 118 ] is crucial to guarantee safe and effective uses of ChatGPT in the various fields.

Generally, some challenges pertain to the majority of professional uses, while others are specific to particular fields. For instance, in domains where ChatGPT's decisions can have tangible real-world impacts, like patient interactions, transparency should be needed to maintain accountability and prevent inadvertent harm. In addition, in creative domains and in programming assistance, the issue of copyright related to the source materials that ChatGPT uses for information should also be taken into account. In the educational field, the fact that a student can produce written work using ChatGPT with little to no effort is a potential issue that educators should consider and address. An important concern across various fields is privacy, particularly with regard to the information that ChatGPT learns from its dialogues - an area that is not yet well understood. Finally, there are concerns about validity and accuracy of the model's predictions and responses.

In this paper, following a detailed exploration of ChatGPT's capabilities, we address the ethical and practical challenges associated with its use, such as privacy issues, algorithmic biases, and the necessity for transparency in AI technology applications. In doing so, we aim to establish a framework for the responsible incorporation of AI technologies across different sectors, leading to enhanced outcomes for individuals and society collectively.

Several overviews on ChatGPT were already published, discussing its wide range of conversational abilities and common applications [ 47 ]. The overview of Zhang et al. [ 115 ] provides a brief technical description about openAI technology, and in particular, about the technology behind ChatGPT. Similar concept was taken by Ray [ 90 ], concentrating on ChatGPT development process, current abilities and achievements, as well as comparing it to other popular LLMs. Both studies provide a list of popular uses in different areas, followed by ChatGPT technical limitations and ethical concerns.

In their extensive review, Ali Khowaja et al. [ 57 ] thoroughly examine a range of concerns related to the use of ChatGPT, including privacy, copyright issues, digital divide, the risk of plagiarism, biased responses, dissemination of false information, and the lack of accountability. The authors propose suggestions to counter each of these issues. It is important to note that while Khowaja et al. mainly focus on the ethical challenges at regulatory and institutional level, our research offers practical tools and insights particularly customized for the individual user, with the aim to optimize his/her benefits while adeptly handling the model's practical limitations.

Several works have studied the extent to which the output of LLMs resembles human-generated text. Cai et al. [ 21 ] examine the similarity between the language use of ChatGPT and humans. They study features like syntactic structures, lexical choices, and semantic coherence. Their research underscores the importance of understanding the nuances and intricacies of how LLMs, such as ChatGPT, generate language and how closely this mirrors human linguistic patterns.

Leong & Linzen [ 63 ] explore the capacity of LLMs to learn exceptions to syntactic rules, focusing on passive sentence structures. Their findings suggest that while models like GPT2 can match human judgments on the acceptability of certain passive sentences, they also rely on the frequency of linguistic patterns in their training data. This provides insights into how these models might be leveraging distributional properties of their input to learn both rules and exceptions.

Other studies highlight scenarios where LLMs might face challenges in accurate expression. Arehalli et al. [ 4 ] consider syntactic surprisal from neural models, revealing that while these models can predict human processing difficulty arising from syntactic ambiguities, they tend to underestimate the actual challenges humans face. This suggests that while LLMs can generate syntactically coherent text, there might be a gap in how they anticipate human syntactic processing compared to actual human behavior.

Mahowald et al. [ 72 ] provide a cognitive perspective on the dissociation between language and thought in LLMs. They distinguish between ‘formal linguistic competence’ and ‘functional linguistic competence’. While LLMs exhibit impressive formal linguistic skills, they often falter when tasks require broader cognitive abilities, such as world knowledge or social cognition. This distinction underscores the challenges and potential avenues for making LLMs more human-like in their language understanding and generation.

Qui et al. [ 88 ] conduct three experiments comparing humans and ChatGPT in the context of generalized conversational implicatures. Their results demonstrate that although ChatGPT might surpass humans in some linguistic tasks, it does not closely resemble human computation of generalized conversational implicatures. Overall, while LLMs like ChatGPT have made significant strides in generating human-like text, there remains a nuanced gap between their linguistic outputs and genuine human language processing and understanding. Addressing these gaps requires a deeper exploration of both the syntactic and cognitive aspects of language.

Proceeding to examine ChatGPT's versatility, we now turn our attention to studies that highlight its applicability across diverse domains. Megahed et al. [ 76 ] investigates the effectiveness of ChatGPT in supporting software process improvement practices, learning, and research. Their study finds that ChatGPT performs well for structured tasks like code translation and explaining well-known concepts, but faces challenges with more nuanced tasks such as explaining less familiar terms and generating code from scratch. The researchers suggest that while AI tools like ChatGPT can enhance efficiency and productivity, caution must be exercised as they can produce inaccurate or misleading results. They recommend validating and complementing ChatGPT's use in software process improvement with other methods to ensure accuracy.

A group of 43 experts [ 32 ] in diverse fields published a detailed report concerning the potential of ChatGPT to enhance productivity and offer gains in industries like banking, hospitality, and information technology. In addition, they also discuss limitations, disruptions, privacy concerns, biases, misuse, and misinformation associated with the technology. several research questions have been proposed, such as examining biases in generative AI, determining optimal combinations of human and AI interaction, and addressing ethical and legal issues.

Our paper's unique characteristic lies in its comprehensive portrayal of how ChatGPT can be strategically utilized, within the confines of the system's limitations. We furnish potential users with an array of heuristics and flowcharts for guidance, in addition to proposing strategies for addressing the ethical dilemmas that accompany its usage. The remainder of this paper is laid out as follows: Section 2 offers an overview of the possible applications of ChatGPT in a diverse range of fields such as researcheducation, healthcare, programming assistance, and mathematics. The technical limitations of ChatGPT are addressed in Section 3. Subsequently, Section 4 introduces techniques and methods that can aid users in maximizing ChatGPT's potential despite its inherent limitations. Finally, we draw conclusions and suggest future directions for research in Section 5.

The advent of advanced chatbots, constructed on the foundations of large language models (LLMs), and significantly fortified by voluminous training data, has ushered in a new era of digital interaction. Their proficiency in understanding and generating natural language has seen them evolve into highly versatile tools, with a broad spectrum of applications spanning numerous industries. Such a tool, exemplified by ChatGPT, possesses tremendous potential that is progressively being recognized, explored, and exploited across an array of sectors in our economy.

In the forthcoming section, we undertake a comprehensive analysis of ChatGPT's versatile applications across various domains. This encompasses its prospective impact on scientific research, educational programs, the healthcare sector, programming support, and contributions to mathematical education and problem-solving initiatives.

In each of these respective areas, we delve into an analysis of how ChatGPT can be harnessed to augment human capabilities, thereby leading to more efficient, innovative, and fruitful outcomes. Concurrently, we also discuss the pertinent challenges associated with its use in these fields, highlighting the importance of addressing these challenges to ensure the effective, safe, and ethically sound utilization of this groundbreaking technology. This comprehensive approach aids in providing a holistic understanding of the capabilities, potential applications, and the attendant considerations of using ChatGPT across various professional domains.

2.1 Research and Academic Usage

The research process is a systematic approach designed to respond to relevant questions or tackle particular challenges by carefully gathering and examining data. It encompasses distinct phases, each presenting unique challenges that scholars must navigate. These phases include identifying a research problem, reviewing existing literature, formulating hypotheses, designing a methodology, collecting data, analyzing and understanding the results, and finally disseminating and publishing the findings. ChatGPT can serves as a beneficial resource for researcher at all phases of the research project, providing relevant information, guidance, and support to optimize efficiency and effectiveness. During the literature review phase, ChatGPT can aid researchers by suggesting relevant topics, questions, and methods within their research area and by summarizing important background and related studies [ 56 , 62 ]. This assistance contributes to the construction of a comprehensive literature review and expedites the gathering and analysis of existing literature.

When researchers are in the data collection phase, ChatGPT can share insights on efficient and reliable data collection methods. It can also furnish information on data quality assessment and provide tips to avoid typical data collection errors. When it comes to data analysis, researchers can prompt ChatGPT to propose suitable analysis methods based on the research question and the type of data. It can also provide guidance on interpreting and effectively presenting the results. ChatGPT proves advantageous even in the final stages of a research project, where it assists in drafting and language editing to enhance readability and grammatical accuracy.

However, it is worth noting that ChatGPT can occasionally make significant errors, which could potentially mislead those who are not experts in the field. These errors are often coupled with detailed explanations that may initially seem plausible, but are ultimately incorrect. ChatGPT has been observed to provide inaccurate quotes, sometimes attributing them to non-existent sources [ 33 ].

Lund and Wang [ 71 ] discuss various benefits of ChatGPT, including enhancing search, discovery, reference services, cataloging, metadata generation, content creation, and paper writing. Ethical considerations, such as privacy and bias, are also addressed. Salvagno et al. [ 95 ] overview the potential assistance of ChatGPT in the writing process of a scientific paper. In addition, they highlight ethical issues, such as the risk of plagiarism and inaccuracies, as well as a potential imbalance in its accessibility.

In scientific data collection, researchers frequently require ratings or classifications for their data, making ChatGPT a valuable resource. The task of ranking and classifying data is crucial, especially in data science. The study of [ 53 ] is conducted to assess ChatGPTs ability to rank content. To evaluate ChatGPT's ranking capability, a test set with diverse prompts is created. Five models generate responses, and ChatGPT ranks them accordingly. The results demonstrate a certain level of consistency between ChatGPT's rankings and human rankings. This initial experiment suggests that ChatGPT's zero-shot ranking capability could alleviate the need for extensive human annotations in various ranking tasks. The paper [ 114 ] deals with text classification tasks. In particular it considers the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Their experiments show that ChatGPT can achieve high performance for commonly used datasets and at the same time, can provide explanation for its own prediction, which is beyond the capability of any existing model. The paper concludes that ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field.

In an increasingly globalized scientific community, the need for effective and accurate translation is paramount. It aids in the dissemination of research findings across different languages and cultures, promoting a greater exchange of ideas and collaboration among researchers worldwide. With this in mind, a preliminary study on ChatGPT for machine translation [ 54 ] provides insightful observations. The authors examine the performance of ChatGPT across various translation scenarios such as translation prompts, multilingual translation, and translation robustness. Findings indicate that ChatGPT can compete with commercial translation tools like Google Translate, particularly for high-resource European languages. However, when it comes to low-resource or linguistically distant languages, there's a notable gap in performance. This poses a challenge in scientific contexts where research could be published in any of these less-resourced languages. Interestingly, the introduction of the ChatGPT4 engine has significantly improved ChatGPT's translation performance. It now stands on a par with commercial translation products, even for those distant languages. This improvement signifies a promising development for the scientific community as it opens up new possibilities for cross-lingual understanding and collaboration.

It is also worth mentioning that the capabilities of ChatGPT can be substantially enhanced by plugins, which are additional software components or scripts that can be integrated with it, some of which can be useful for conducting research. Awan [ 7 ] provides a list ChatGPT plugins for Data Science. In the following, we specify some of the useful plugins that can be helpful for general research; some plugins that are more relevant to specific fields (e.g., mathematics or programming) will be mentioned in their respective sections.

One such plugin is the Advanced Data Analysis plugin, formerly referred to as the Code Interpreter [ 59 ]. This plugin is adept at extracting data from a diverse array of sources and formats, facilitating data analysis and the creation of various charts and diagrams for visual representation. Notably, it can autonomously decipher and interpret data, streamlining the analytical process considerably. Researchers can leverage specific plugins to access the most current data from the web, such as WebPilot [ 6 ] and ScholarAI [ 70 ].

To summarize, ChatGPT can be a beneficial tool for researchers, assisting in the different phases of a research project, from formulating research questions to drafting and language editing. ChatGPT has shown potential in a variety of applications, such as content summarization and ranking, text classification, and machine translation. However, it can also be prone to significant errors, which could mislead non-experts, such as providing inaccurate references.

2.2 Education

In the swiftly advancing world of technology, especially in the domain of intelligent chatbots, education emerges as one of the most influential and challenging fields. Within this sector, there is a dual objective: firstly, to grant students access to a broad spectrum of information and a diverse range of tools to enhance their capabilities; and secondly, to uphold integrity, foster independent thinking, and encourage the cultivation of their innate abilities.

Consequently, employing ChatGPT in education unveils a complicated dichotomy; on one hand, it bolsters learners with powerful tools and an enriched reservoir of knowledge, amplifying their intellectual curiosity and acumen. On the other hand, in this era of unprecedented information availability, the widespread access to a plethora of information, including readily available solutions to exercises, poses a significant challenge to conventional teaching methods. Empowered by the ubiquity of the internet, young learners can easily transcend the confines of the classroom, prompting educators to reassess and fine-tune their pedagogical strategies. This juncture of opportunities and predicaments in the technology-enhanced educational landscape triggers a robust conversation on the future trajectory of teaching and learning.

Two additional considerations need to be addressed. The first pertains to the educational challenges posed by potentially partial, unreliable, or incorrect responses students might receive when interacting with chatbots. However, this concern is expected to diminish as technology progresses. The second consideration revolves around the changing role of educators in a world increasingly dominated by AI, particularly conversational technology such as ChatGPT. This concern grows more acute as technological advancements continue. The abilities of ChatGPT, which include providing detailed explanations across a wide range of fields, have led to conjecture about its potential to supplant traditional teaching methods in some areas.

In [ 80 ], the authors conduct a literature assessment on the educational implications of artificial intelligence, focusing on OpenAI's ChatGPT. The study highlights the advantages of ChatGPT, an AI-powered tool, in improving student access to information by providing immediate, tutor-like responses, particularly benefiting self-paced learners. However, it also acknowledges its limitations, such as occasionally producing incorrect or illogical responses, and sensitivity to input phrasing. For example, while it might not answer correctly with one phrasing, a slight rephrase can elicit a correct response.

In [ 67 ], an extensive literature review is carried out within the initial three months following the release of ChatGPT. The review rigorously examines the capabilities of ChatGPT, its potential roles in the educational sector, and pertinent concerns associated with its use. The analysis underscores the potential of ChatGPT as a valuable tool for instructors, providing a basis for the development of course syllabi, teaching materials, and assessment tasks. However, the study also addresses concerns regarding the accuracy and reliability of the content generated by ChatGPT, as well as the challenge of plagiarism prevention. The study emphasizes the need for prompt action by educational institutions to update their guidelines and policies concerning academic integrity and plagiarism prevention. Additionally, it advocates for the training of instructors on the effective use of ChatGPT while equipping them with the skills to identify instances of student plagiarism.

AI-based comprehension abilities could serve as valuable educational tools for detecting and analyzing classroom social dynamics and students’ emotional states. Phillips et al.'s study [ 83 ] demonstrates the capabilities of ChatGPT-3 in summarizing student interactions in a game-based learning setting and its precision in identifying various emotions and behaviors. Despite limitations such as understanding context and detecting hyperbole, GPT-3 can provide insightful interpretations of collaborative dynamics, potentially reinterpreting seemingly unproductive conversations as crucial for managing negative emotions. Teachers can employ ChatGPT-3 to monitor real-time student discussions, address students experiencing difficulties, and observe progress. The study also suggests that teachers act as intermediaries between the AI's interpretations and educational decisions, thus mitigating the risk of model failure.

The study by [ 34 ] conducted a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of ChatGPT in the education sector. The strengths of ChatGPT identified in the study include its ability to provide plausible and credible responses compared to other AI tools, its self-improving capability, personalized responses, and its aptitude for understanding complex inquiries and delivering real-time relevant answers. The opportunities presented by ChatGPT in education revolve around increased accessibility to information. It can efficiently find and summarize relevant information, making it easier for students to access detailed knowledge quickly. Moreover, ChatGPT has the potential to offer personalized support and feedback to students at varying levels of complexity. It can also stimulate critical thinking by challenging students with tailored sets of questions corresponding to their proficiency level.

However, the study also highlights several weaknesses of ChatGPT. One weakness is its lack of deep understanding. While it recognizes patterns and generates plausible responses, it does not possess a comprehensive grasp of the underlying concepts. ChatGPT also struggles with evaluating the quality of responses. Additionally, there are inherent risks of biases and discrimination. Biases in training data, algorithmic design, and societal context can perpetuate biases and discrimination within ChatGPT's outputs. Moreover, the use of ChatGPT raises concerns about academic integrity, as it can be exploited for cheating in online exams and compromise assessment security. Ethical issues such as the provision of fake information and similarities to existing sources are also potential challenges associated with ChatGPT. Lastly, the use of ChatGPT by students may lead to a decline in higher-order cognitive skills, including creativity, critical thinking, reasoning, and problem-solving.

Delving into each domain separately brings with it a unique blend of opportunities and challenges. We proceed by dissect research that explores the outcomes of incorporating ChatGPT into an array of educational sectors. This nuanced approach aids in illuminating the specific implications of AI integration within these distinct fields.

Cotton et al. [ 25 ] examines the opportunities and challenges of using ChatGPT in higher education, and discusses the potential risks and rewards of these tools. The paper also considers the difficulties of detecting and preventing academic dishonesty, and suggests strategies that universities can adopt to ensure ethical and responsible use of these tools. According to this paper, the integration of chatAPIs and GPT-3 in higher education holds significant potential for enhancing student engagement, collaboration, and accessibility. The use of chatAPIs enables asynchronous communication, timely feedback, group work, and remote learning support. Similarly, GPT-3 offers valuable applications such as language translation, summarization, question answering, text generation, and personalized assessments. However, the adoption of these tools also presents challenges and concerns, particularly regarding academic integrity and plagiarism. The use of chatAPIs and GPT-3 can potentially facilitate cheating, and distinguishing between human and machine-generated writing can be challenging. To ensure ethical and responsible usage, universities need to carefully evaluate the risks and rewards associated with these tools. This involves developing policies and procedures, providing training and support for students and faculty, and implementing robust methods to detect and prevent academic dishonesty. By addressing these challenges, universities can harness the opportunities offered by chatAPIs and GPT-3 while upholding the integrity of their assessments and maintaining the quality of their educational programs.

Another potential usage of ChatGPT is to guide students and teach them some mathematical concepts. In [ 82 ] the authors check the learning gain evaluation of ChatGPT by comparing the efficacy of its hints with hints authored by human tutors, across two algebra topic areas, Elementary Algebra and Intermediate Algebra. All their experiments produced learning gains; however, they were only statistically significant among the manual hint conditions. Manual hints produced higher learning gains than ChatGPT hints in both lessons and these differences were statistically significantly separable. They found out that the technology, in its current form, still requires human supervision. Their result showed 30 % rejection rate of produced hints based on quality, suggesting that All of the rejected hints were due to containing the wrong answer or wrong solution steps. None of the hints contained inappropriate language, poor spelling, or grammatical errors.

The research conducted in [ 87 ] explores the use of generative AI and ChatGPT techniques in the context of engineering education. The authors engage ChatGPT by posing various questions related to this topic and analyze its responses. They ultimately conclude that ChatGPT and similar AI language models hold significant potential as convenient and beneficial tools for both students and teachers in engineering education. These models have the ability to generate text resembling human-like conversation, provide answers to questions, compose essays, and assist with homework assignments. Potential applications encompass language editing, virtual tutoring, language practice, generating and solving technical and nontechnical queries, as well as aiding in research tasks. However, it is crucial to recognize that ChatGPT and other AI language models are not flawless and can produce errors or furnish incorrect information. Therefore, it is imperative to exercise caution when using these tools and establish community guidelines and standards to ensure their fair and responsible use.

The paper [ 26 ] discuss the benefits and limitations of ChatGPT in Business education and research specifically focusing on management science, operations management, and data analytics. The study considers how professors and students can utilize ChatGPT in these areas. Professors can leverage ChatGPT to design courses, develop syllabi and content, assist with grading, and enhance student comprehension. On the other hand, students can rely on ChatGPT to explain intricate concepts, aid in code creation and debugging, and generate sample exam questions. The primary strength identified in this analysis is ChatGPT's proficiency in writing and debugging code, making it particularly valuable for educational and research purposes. However, it is essential to acknowledge that ChatGPT does have limitations, including occasional errors and a requirement for a deeper or advanced domain knowledge. Additionally, the discussion surrounding ChatGPT in business education and research raises concerns regarding potential biases and issues related to plagiarism.

We proceed by describing two additional studies that propose various strategies to address the integrity challenges. The study conducted by Ryznar et al. [ 92 ], explores diverse methods to maintain the integrity of examinations in the era of open AI technologies, including ChatGPT. This research presents a comprehensive range of strategies to safeguard exam integrity, which encompass high-tech solutions such as video proctoring and specialized exam software, as well as low-tech measures like time constraints and meticulous course design. By presenting this array of strategies, the study aims to illuminate effective practices for exam administration that uphold integrity despite the widespread use of technologies like ChatGPT.

In a separate study, Shidiq et al. [ 100 ], scrutinize the impact of the ChatGPT system on students’ writing skills, with a particular focus on creativity. While the capacity of ChatGPT to generate responses based on inputted keywords has potential benefits for education and learning, the researchers note that not all aspects of this technology necessarily contribute effectively to the development of a diverse range of student skills, including creative writing. To address this, the paper underscores the importance of educators implementing strategies that go beyond online learning tools, which students may exploit while completing assignments. One proposed approach involves using paper as a platform for task development, serving as a mechanism for process control and specific assessment of creative writing tasks. When this method is implemented, it enables teachers to offer a structured framework that can guide students and assess their progression in creative writing.

Finally, we mention recent work that proposes the use of two ChatGPT based agents for assisting in teaching classroom material, one taking the role of a student and the other taking the role of a professor [ 97 ]. The human student is required to “teach” the material to a human-bot, while the professor-bot supervises the entire interaction, and ensures that the human student provides correct information.

Some plugins that can be useful for educators and students, are the Open Lecture plugin for accessing study materials, the Videoinsights plugin for summarizing videos, Quick Recall for creating flashcards, and the Speak plugin for language learning. Canva is a plugin for designing presentations, and the ShowME plugin is designed for assisting in diagram creation.

In conclusion, ChatGPT proves to be a valuable tool for lecturers and instructors to structure their lessons, offering personalized feedback and serving as a starting point for course development. It surpasses mere Google search responses, providing improved student access to information and delivering personalized support with a personal touch. Moreover, it enhances student engagement, collaboration, and accessibility, enabling students to rely on it for explanations, code generation, and sample test questions. Overall, ChatGPT empowers instructors and students across various educational domains.

However, there are certain limitations and concerns associated with ChatGPT. One concern is its potential to generate incorrect or fabricated information, which can have implications for academic integrity. Despite its ability to produce plausible responses, ChatGPT lacks a deep understanding and may provide responses that are incorrect or illogical. It is also sensitive to changes in input phrasing or multiple attempts at the same question, as a slight rephrasing can lead to an accurate response. Additionally, not all aspects of ChatGPT effectively contribute to the development of various student skills, particularly in the realm of creative writing. While it can recognize patterns and generate plausible responses, it lacks a comprehensive grasp of underlying concepts and struggles with evaluating the quality of its responses.

Moreover, there are inherent risks of biases and discrimination in ChatGPT's outputs, stemming from biases in training data, algorithmic design, and societal context. Furthermore, the use of ChatGPT raises concerns about academic integrity, as it can be exploited for cheating in online exams and compromise the security of assessments. Ethical issues, such as the provision of fake information and similarities to existing sources, pose additional challenges associated with ChatGPT.

2.3 Healthcare

The healthcare sector harbors unique nuances, primarily due to the significant repercussions of medical interventions and the extensive array of ethical dilemmas intertwined with medical procedures. In parallel, ChatGPT exhibits marked expertise in the spheres of medicine and healthcare, holding substantial potential to assist healthcare professionals through its advanced capabilities.

As demonstrated in a study by Kung et al. [ 60 ], ChatGPT's aptitude in processing intricate medical and clinical information resulted in a commendable performance on the United States Medical Licensing Examination (USMLE), often surpassing the passing threshold. Intriguingly, despite its general content training, ChatGPT outperformed PubMedGPT, a model specifically trained on biomedical literature, indicating that a combination of broad-based and domain-specific models could substantially boost accuracy. In the study of Johnson et al., [ 55 ] 33 physicians across 17 specialties generated 284 medical questions and graded ChatGPT-generated answers to these questions for accuracy. They found out that ChatGPT generated accurate answers and got completeness scores across various specialties, question types, and difficulty levels.

Nonetheless, despite its impressive proficiency in the medical field, it's crucial to understand the potential limitations of its application, especially within such a high-risk domain where issues of responsibility are heightened. Of particular concern is the use of ChatGPT as a tool for medical consultation among non-professional users. It's vital to underline that this AI should not be regarded as a substitute for professional medical advice.

Sallam [ 93 ] provides an exhaustive review of the potential applications and drawbacks of ChatGPT within healthcare education, research, and practice. On the beneficial side, Sallam points out ChatGPT's contribution to enhancing scientific writing, promoting research versatility, facilitating data analysis, aiding in literature reviews and drug discovery, streamlining workflow, and fostering personalized learning in healthcare education.

However, the review also underscores various potential pitfalls including ethical and legal dilemmas, copyright and transparency issues, risk of bias and plagiarism, possible generation of inaccurate content, cybersecurity threats, and the danger of information overload, or ‘infodemics’.

Sallam further stresses concerns about the use of ChatGPT in healthcare, specifically its lack of personal and emotional perspectives crucial for healthcare delivery and research. The review also points out potential challenges in healthcare education, such as the quality of training datasets possibly leading to biased and outdated content. Lastly, limitations of ChatGPT, such as its current inability to handle images, underperformance in certain areas (as illustrated by its failure to pass a parasitology exam for Korean medical students), and potential plagiarism issues, are discussed.

Biswas [ 17 , 16 ] examined the uses of ChatGPT both in public health and in medical research. In [ 17 ], the potential applications of ChatGPT in public health are scrutinized. The paper underscores ChatGPT's capabilities, such as dispensing information on public health issues, fielding questions on health promotion and disease prevention strategies, clarifying the role of community health workers and health educators, debating the impact of social and environmental factors on community health, and providing insights about community health programs and services. Nevertheless, using ChatGPT in this area isn't without constraints, including issues of limited accuracy, inherent biases and data limitations, context insensitivity, diminished engagement, and the lack of direct communication with health professionals.

In another study, Biswas [ 16 ] explores the potential advantages and ethical concerns associated with using ChatGPT in medical research. Among the ethical issues raised are questions of authorship, accountability, and authenticity. Legal challenges, particularly around copyright infringement and specific field regulations, are also noted. Potential drawbacks, such as stifled innovation due to repetitive text generation and decreased student engagement, are considered. Moreover, concerns regarding accuracy and bias in AI-generated text are brought to light, as AI models trained on large, potentially biased datasets could inadvertently perpetuate or amplify such biases.

Despite ChatGPT's ability to provide medical information, respond to medical inquiries, and suggest differential diagnoses for common symptoms, substantial concerns persist about its decision-making process and outdated information. As highlighted by Arif et al. [ 5 ], employing ChatGPT in healthcare presents significant obstacles, predominantly due to its decision-making process and outdated data. The authors question ChatGPT's lack of critical thinking and its habit of presenting information redundantly and irrationally. The model's training data, only updated until 2021, and its restricted access to major medical databases such as PubMed and Cochrane, are noted as significant hurdles. These constraints not only limit its applications to tasks like abstract writing, but they also raise questions about its overall credibility.

In the study by Van dis et al. [ 109 ], numerous instances of inaccurate and misleading content generated by ChatGPT were identified. These included numerical mistakes, factual errors, misrepresentations, and incorrect data, particularly when posing questions related to psychiatry. In essence, the issues of inaccuracy and misinformation observed in other fields are also evident in the healthcare domain, despite extensive training in this area.

However, when carefully used, ChatGPT can be useful despite its limitations. In the contents of healthcare and health research, some valuable plugins for healthcare are detailed by [ 102 ]. The healthcare-focused plugins include the Clinical Trial Radar [ 49 ] for updated global clinical trials and related data, and the Polarr ChatGPT Plugin, which can enhance medical imaging through advanced image editing.

In summary, ChatGPT has exhibited considerable capabilities in the realm of healthcare, offering considerable assistance to professionals by deciphering intricate medical information. It has displayed an admirable performance in medical examinations and has proven accurate in addressing various medical queries across numerous specialties.

However, there are ongoing concerns about its lack of personal and emotional insights crucial to healthcare delivery, potential biases in training datasets, difficulties with image handling, and possible plagiarism. In medical research, it grapples with ethical and legal challenges, such as issues concerning authorship, authenticity, and copyright infringement.

Additional concerns encompass the possibility of generating inaccurate content and drawing incorrect conclusions. Moreover, the reality that the training data doesn't extend beyond some constate date serves to restrict its practical usage and casts doubt on its dependability.

As a result, while ChatGPT may offer valuable perspectives in public health and medical research, it's essential to understand that it should not replace professional medical advice due to concerns surrounding its decision-making capabilities, potential bias, and outdated information.

2.4 Programming Assistance

The programming field is distinct primarily due to the fact that ChatGPT developers, like those of other AI tools and various plugins, are themselves programmers. Consequently, they have a deep understanding of their own needs and insights into how the tool can aid them in the development process. Given this, it is unsurprising that a significant number of articles and blogs have chronicled ChatGPT's contributions to the development landscape, elucidating its potential applications through specific use cases and detailed examples [ 52 , 107 , 64 , 96 , 106 , 103 , 42 ]. In the subsequent sections, we intend to further expound on this topic, delineating it in accordance with various developmental stages.

ChatGPT can serve as a valuable tool for programmers throughout the software development process, providing suggestions, guidance, and feedback that contribute to enhanced efficiency and effectiveness in programming. The key principle in utilizing ChatGPT for programming assistance involves supplying it with appropriate prompts to achieve desired outcomes [ 38 , 69 , 86 ]. However, it is important to note that, as demonstrated in previous publications and in this study, effective use of ChatGPT for professional development in software engineering requires significant proficiency in both interacting with AI tools such as ChatGPT and in software development skills.

Toam et al. [ 106 ] conducted an empirical study on ChatGPT to assess its potential as a programming assistant. They focused on three code-related tasks: code generation, program repair, and code summarization. For code generation, ChatGPT performed well on common programming problems but struggled with generalization to new problems. In program repair, ChatGPT achieved competitive results compared to a state-of-the-art tool. However, it had a limited attention span and performed worse when provided with unrelated prompts. The study highlights the importance of prompt engineering and provides insights into ChatGPT's practical applications in software engineering.

Xu Hao, the Head of Technology for China at Thoughtworks, demonstrated the use of ChatGPT in the programming process [ 38 , 69 ]. He began by setting the context for the application and defining the desired code structure through a prompt, then detailed the system's common implementation strategy and requirements. Xu used ChatGPT to create a solution outline without producing code, which he reviewed and revised to align with the architectural vision. Next, he had ChatGPT generate a detailed plan, including component names, methods, and props. He also requested test examples and code implementation from ChatGPT, prioritizing tests first. Lastly, he evaluated the generated code, refining it using his expertise and the provided examples. In essence, Xu showcased how ChatGPT, when used effectively in conjunction with expert knowledge, can significantly enhance the software development process.

The primary strategy recommended by experts for generating innovative results involves a method called “Knowledge Generation”. This approach entails formulating lengthy and complex prompts to elicit functional code [ 38 ]. These prompts encompass architectural descriptions, design guidelines, and step-by-step action plans, emphasizing the importance of testing and thorough code inspection. Experts highlight that programming with ChatGPT is a unique form of coding that relies heavily on a comprehensive understanding of architecture, system knowledge, and rigorous testing. While this approach offers potential time-saving benefits and automation of certain tasks, it still requires learning and mastery as a skill. The blog [ 86 ] provides a comprehensive collection of commonly used prompts to effectively utilize ChatGPT for programming assistance. Additionally, [ 78 ] offers ten insightful suggestions for intelligent and efficient utilization of ChatGPT.

The rest of this section highlights various practical applications of ChatGPT for programming objectives. These applications draw inspiration from various authors, bloggers, and our own experiences.

Enhance programming skills: [ 37 , 50 ]: ChatGPT offers a time-saving solution for learning programming languages by aggregating information from various sources and presenting it in a single interface. It offers code explanations, alternative approaches, and serves as a real-time tutor. Programmers may use ChatGPT as an online teacher to enhance their programming skills [ 64 ]. This can be done through code explanations and by introducing relevant technologies, coding methods, and software packages. Additionally, ChatGPT can provide feedback and recommendations on the code, aiding in understanding errors and potential enhancements [ 37 ].

Moreover, ChatGPT's ability to generate code and provide valuable explanations enables developers who are unfamiliar with a programming language or framework to quickly catch up without spending excessive time on the fundamentals. This is particularly valuable for beginner programmers seeking to accelerate their learning process [ 42 ]. To further this objective, the ChatWithGit plugin is designed to aid programmers in enhancing their coding expertise by retrieving applicable code excerpts from GitHub.

Information gathering: ChatGPT is able to provide relevant information and explanations on complex programming concepts [ 96 ]. By utilizing ChatGPT, developers can quickly obtain answers to specific technical questions, access relevant code samples, and save time searching for solutions or examples on the internet. In particular, ChatGPT can be used to explore libraries and resources, especially in the context of intelligent web page data extraction [ 43 ].

Code explanation: When asked to explain code, ChatGPT has the capability to provide detailed natural language explanations [ 96 ]. However, it's important to note that, based on our experience, these explanations can sometimes be technical and may not always capture the intended meaning of the code. For example, if the code contains inappropriate variable names, the explanations may focus on those names instead. Additionally, there is a possibility of incorrect explanations, as shown in Section 3.1.1, so caution should be exercised when relying on the chatbot for code understanding [ 8 ].

Code generation: ChatGPT is a powerful tool for developers, leveraging natural language processing to understand and interpret developer needs. It can save programmers time by assisting with repetitive tasks and boilerplate code [ 42 ]. It is useful for scaffolding [ 96 ] and generating foundational code elements, helping overcome the Cold Start Problem [ 52 ].

ChatGPT's programming capability extends to various programming languages, both old and new [ 43 ], and it can even convert code from one language to another [ 8 ]. This enhances its coding potential and enables seamless transitions between languages. In addition, ChatGPT proves adept at generating website code when provided with detailed prompts [ 64 ], making it a valuable tool for web development tasks. It also demonstrates understanding of Linux [ 64 , 8 ] and SQL [ 8 ] command lines, allowing it to interpret and respond to queries and commands in these domains. This expands its usefulness in assisting with Linux-based operations and interacting with SQL databases.

In addition to previously mentioned plugins, the Noteable Notebook plugin facilitates computational notebook functions in ChatGPT, allowing data analysis and machine learning model training, while the Advanced Data Analysis plugin offers real-time Python code execution in a virtual environment.

While ChatGPT has been rapidly adopted in the industry, caution is advised when using AI-generated code in critical software systems [ 107 ]. It is recommended to use ChatGPT as a companion tool, with human programmers retaining control over the development process, since trying to outsource the entire software process to Chatbot can be a recipe for disaster [ 107 ].

Although ChatGPT's coding level is observed to be comparable to that of a first-year programming student, with a lack of expertise and diligence [ 43 ], it still provides value by assisting with code writing and information lookup. While it may not be able to handle complex projects on its own, it can enhance productivity and efficiency in software development [ 8 ].

“Conversational Coding” refers to the process of using ChatGPT for code generation, allowing developers to leverage their critical thinking skills without the need to directly translate their thoughts into code [ 51 ]. By prompting ChatGPT with their intentions and engaging in dialogue, the developers can collaborate with the model to refine and improve the generated code. If any issues arise, the developers can report them to the chatbot, prompting it to provide updated code. This iterative process enables effective collaboration between developers and ChatGPT to achieve desired results.

A suggested approach for utilizing ChatGPT in data science [ 52 ] is to use it as a high-order functional unit. ChatGPT can generate code based on specified parameters and adapt it for different learning methods, enabling code reuse, automation, and time-saving in adapting code for various models. This usage of ChatGPT resembles the behavior of a function, providing functionality tailored to specific requirements.

In Section 4, we present some flowcharts that can be useful for problem-solving with ChatGPT. These flowcharts can be used to describe iterated sessions of requests, code generation, careful review, and code refinements, leading to the desired results.

Code debugging: One of the common uses of ChatGPT, which is widely suggested by programming blogs, is programming debugging [ 96 , 64 , 42 , 103 ]. They note that ChatGPT is a powerful tool for identifying coding errors, ranging from simple syntax mistakes to complex logic errors. Developers can provide problematic code to obtain error detection assistance and further guidance by describing desired outcomes and current outputs [ 107 ].

ChatGPT's human-like interface provides succinct explanations and integrates additional hints, resulting in a significantly improved success rate in resolving programming issues [ 8 ]. Detailed information, such as programming language and environment, enhances bug hunting with ChatGPT [ 107 ]. It examines bugged code, suggests actions for bug identification and correction, and proposes modifications to enhance readability and maintainability, reducing the occurrence of bugs and expediting development cycles [ 42 ]. However, since mistakes are still faced, careful use of ChatGPT is important, and its outputs should be validated [ 103 ].

In an experimental evaluation by Sobania et al. [ 101 ], ChatGPT's bug fixing abilities were compared with standard methods. ChatGPT performed competitively with deep learning-based approaches and outperformed traditional program repair methods, achieving a 77.5% success rate. The study highlighted the value of human input in improving an automated program repair system, with ChatGPT facilitating collaboration. The authors acknowledged that the mental effort required to verify ChatGPT's answers can be significant, suggesting the integration of automated approaches to provide hints and verify responses, thereby enhancing ChatGPT's performance and making it a practical tool for software developers in their daily tasks.

Surameery et al. [ 103 ] discuss the characteristics of ChatGPT in providing debugging assistance, bug prediction, and bug explanation. They highlight its potential in these areas while acknowledging the importance of using other debugging tools and techniques for validation. The paper concludes by suggesting that ChatGPT can be a valuable component of a comprehensive debugging toolkit, complementing other tools to effectively identify and fix bugs.

To summarize, ChatGPT is found to be a powerful tool for programming debugging, capable of identifying coding errors and providing guidance from syntax mistakes to complex logic issues. The human input is valuable in verifying ChatGPT's answers and providing additional context or insights that may aid in bug resolution. By combining the automated assistance of ChatGPT with human expertise and validation, developers can effectively collaborate to identify and fix bugs in a more efficient and accurate manner.

Code optimization: ChatGPT possesses the ability to analyze user-provided code and suggest improvements in terms of efficiency, security, and readability [ 42 ]. Developers can prompt ChatGPT to propose optimization techniques or generate optimized versions of code, with the AI model providing explanations for its corrections and highlighting areas of potential improvement [ 107 ].

Moreover, ChatGPT can generate alternative code that enhances efficiency, scalability, and performance across different programming languages and patterns, leading to more effective and maintainable code [ 42 ]. It can also rewrite code to improve coding style, simplicity, and other desired aspects based on the specific requirements and preferences of programmers [ 96 ].

For optimal results, it is advisable to formulate precise and specific queries when interacting with ChatGPT, as the suggested corrections and recommendations depend on both the inputted code and the context of the query [ 64 ].

Data formatting and data creation: ChatGPT can also be utilized for data formatting tasks, such as structuring data into specific formats like CSV or JavaScript objects, and generating filler content [ 107 ]. It has the capability to create regular expressions [ 107 ] and generate formatted content in various formats like LaTeX, HTML, and others. Furthermore, ChatGPT can generate random numbers following specific statistical distributions, which can be beneficial for data augmentation in training machine learning models [ 27 ].

Test cases generation: One effective approach to ensure bug-free and robust code that handles exceptions and edge cases is to write unit tests. ChatGPT can be a useful tool for this task as well [ 107 ]. Developers can leverage ChatGPT to assist them in writing test cases for specific functions by providing the relevant code and detailed instructions [ 96 ]. ChatGPT can generate test inputs and expected outcomes, covering various code paths and edge cases. It can also aid in creating concise and comprehensible documentation for test cases, including inputs, predicted outcomes, and pass/fail conditions [ 42 ]. While ChatGPT can automate the process of writing test cases, it is still advisable to review and verify the generated test cases rather than relying solely on them.

Project Documentation: ChatGPT is a versatile tool that excels at generating comprehensive code documentation [ 96 ]. It can provide detailed information, incorporate usage examples, and explain code in plain English to assist developers in understanding its functionality and purpose [ 64 ]. Leveraging its natural language processing abilities, ChatGPT accurately identifies code requirements and generates informative documentation, including automatic comments, to aid future development [ 42 ].

The Show Me plugin is useful during project documentation and design phases, facilitating the creation of flowcharts and diagrams to visually represent data flows, thereby enhancing understanding and aiding in detailed project design.

Code translation: ChatGPT offers valuable assistance in code translation, enabling the smooth transfer of code from one programming language to another [ 96 , 64 ]. This functionality proves useful when encountering programming challenges in one language and seeking solutions in different languages [ 107 ]. Specifically, it provides significant benefits to users who need to migrate applications from a mainframe to a PC-based platform or when dealing with unsupported languages [ 85 ].

A general comment As mentioned by [ 50 ] and other programming experts, ChatGPT is not infallible. Like any AI system, it can make mistakes and may exhibit unwarranted confidence in those mistakes. Therefore, it is recommended to remain vigilant and continue to verify, test, and debug its output.

Another limitation of ChatGPT is its lack of comprehensive context [ 50 , 75 ]. While it can provide code snippets or even entire files, it lacks an understanding of specific conventions, best practices, or project requirements unique to your company or project. It cannot anticipate how the code will interact with other components or consider critical aspects such as performance, security, privacy, and accessibility. Hence, the ultimate responsibility for the code lies with human developers.

However, as stated by [ 64 ], ChatGPT cannot entirely replace programmers as programming requires various skills such as problem understanding, solution design, testing, domain knowledge, and communication.

By effectively utilizing ChatGPT as a valuable tool, programmers can focus on critical thinking and human creativity [ 64 ]. It is advisable to master ChatGPT as a companion to enhance programming work and increase competency, rather than relying solely on it for coding tasks. While ChatGPT provides a powerful means to expedite the coding process, it should not be seen as a magical tool that eliminates the need for human effort and understanding [ 107 ].

To summarize, ChatGPT offers valuable applications for programmers across the software development process. It can enhance programming skills, provide information, explain code, assist in code starting and generation, aid in code debugging and optimization, format and create data, generate test cases, document projects, and translate code from one programming language to another. It is important to utilize ChatGPT effectively, considering prompt engineering and expert knowledge, and to validate its outputs. While ChatGPT has limitations and cannot replace human programmers, it serves as a powerful tool to expedite coding tasks and enhance productivity when used as a companion in the programming workflow.

2.5 Mathematics Tasks

Mathematical materials are widely available on the internet, and unlike fields such as current affairs, which are constantly evolving, mathematics is based on fundamental principles that do not change on a day-to-day basis. Consequently, it would be reasonable to expect that ChatGPT, which has been trained on a vast corpus of text data, which includes a significant amount of mathematical concepts and operations, would be able to comprehend and analyze mathematical problems at least at the undergraduate level, such as linear algebra and calculus. However, in practice, it has been found that ChatGPT's comprehension levels are far from satisfactory, as we will explain in detail below.

In fact, ChatGPT has the ability to perform various mathematical tasks, including solving standard equations, simplifying expressions, computing derivatives and integrals, and performing basic arithmetic operations. ChatGPT can also generate mathematical expressions and equations and is capable of answering questions related to mathematical concepts and theories. Additionally, ChatGPT can provide step-by-step explanations and examples to help users better understand mathematical concepts and problem-solving strategies.

Based on our firsthand experience and a review of the existing literature, it is evident that ChatGPT exhibits an overconfidence in the mathematical domain that exceeds its true abilities. The model lacks sufficient logical analysis and has difficulty understanding algebraic representations that involve parameters or detailed descriptions of group elements, both in algebraic and verbal contexts. When responding to mathematical queries, ChatGPT frequently offers detailed answers, albeit marred by incorrect or inadequate reasoning. The recent studies that we will discuss below underscore this deficiency, illustrating the model's tendency to assert incorrect solutions and create entirely erroneous proofs, all while articulated in convincing mathematical language, especially in situations involving complex calculations and logical reasoning. This issue persists even when the responses are patently incorrect, with the model maintaining a high level of confidence in its assertions.

Azaria [ 10 ] demonstrates the limitations of ChatGPT in processing complex mathematical expressions and its tendency to produce random digits are explored. The paper highlights several difficulties ChatGPT faces in tasks such as multiplying large numbers, computing roots and powers of numbers (especially fractions), and adding (or subtracting) irrational numbers like □ or e. The study notes that ChatGPT is unaware of its limitations and may simply generate random digits when faced with a complex mathematical expression. To analyze ChatGPT's frequency of digit output, the researchers subjected it to mathematical queries that resulted in irrational numbers. The paper also includes an appendix that discusses ChatGPT's responses to common social experiments, demonstrating its tendency to answer like humans.

In the study conducted by Bagno et al. [ 13 ] the researchers examine ChatGPT's performance in the realm of Mathematics Education, specifically in the context of teaching fundamental concepts of Linear Algebra. Although there are instances where ChatGPT provides precise and well-explained responses, the investigation also highlights numerous cases where it commits substantial mathematical inaccuracies and struggles with logical reasoning.

The paper [ 41 ] investigates the mathematical capabilities of ChatGPT. It tested whether ChatGPT can be a useful assistant to professional mathematicians by emulating various use cases that come up in their daily activities. When they evaluate its performance against other mathematical datasets, they found out that that ChatGPT's mathematical abilities are significantly below those of an average mathematics graduate student and that ChatGPT often understands the question but fails to provide correct solutions. They conclude that ChatGPT is not yet ready to deliver high-quality proofs or calculations consistently.

To summarize, the current version of ChatGPT has some limited ability to quote definitions, theorems, and proofs, and it can solve and explain some types of known mathematical equations and challenges, but it fails when some deeper logical understanding is required. These limitations might originate from its essence, structure, and training methodology as a language model. ChatGPT is a substantial language model trained on a vast corpus of text data, learning to predict the subsequent word in a sentence based on the preceding words. While this strategy facilitates the generation of text that is generally coherent and stylistically sound, it can struggle to maintain logical consistency and accuracy in more complex, detailed responses, including those involving mathematical reasoning. This issue represents a significant risk to students who might be misled by its eloquent expression, consequently adopting incorrect conclusions. Therefore, it is imperative for users, especially in fields grounded in logic such as mathematics, to rigorously assess the information provided by ChatGPT, cross-referencing it with multiple sources and applying stringent logical and mathematical analysis to confirm its accuracy.

The Wolfram plugin [ 28 ] can address some of ChatGPT's mathematical limitations by integrating the mathematical abilities of Wolfram Alpha and Wolfram Language platforms. This enhancement improves ChatGPT's ability to accurately compute and solve intricate mathematical problems, making it a valuable tool for mathematical problem-solving.

To summarize, ChatGPT is adept at solving basic mathematical problems and explaining concepts, but it struggles with complex operations and often provides incorrect answers confidently. Its logical reasoning abilities fall short, particularly in algebraic representations and comprehension. As a potential solution, future versions of ChatGPT could be trained more intensively on complex mathematical datasets, with a focus on improving logical reasoning abilities and reducing overconfidence in problem-solving. Additionally, implementing mechanisms for it to be aware of and communicate its limitations could increase its utility and user trust.

As established in previous research [ 14 , 104 , 58 ], ChatGPT has been found to have several limitations. These limitations include the potential to provide incorrect responses, generate inaccurate code, rely on outdated information, have limited logical reasoning capabilities, lack self-correction abilities, and display overconfidence. Additionally, there is a concern about the tendency of ChatGPT to produce biased or inappropriate responses.

Given ChatGPT's ability to provide in-depth explanations, supported by examples and references, it becomes challenging to navigate its occasional inaccuracies, critical errors, and fundamental mistakes.

Therefore, ChatGPT should be utilized with caution, and its outputs should be independently verified using other reliable tools. These limitations position it as a tool that is especially beneficial for those with expertise in the respective fields.

In this section, we will discuss some of the inherent limitations of ChatGPT, and in Section 4, we will present methodologies and strategies that can help address these limitations responsibly and with caution.

3.1 Incorrect Responses-Confidently

ChatGPT faces a noteworthy challenge related to its occasional delivery of inaccurate information while projecting an unwavering sense of certainty. This limitation significantly hampers the platform's effectiveness, necessitating users to approach its responses with caution and independently verify the information provided. This becomes especially crucial in domains where there are clear-cut correct and incorrect answers. For instance, in medical or scientific contexts, the provision of incorrect information by ChatGPT, coupled with its high-level language explanations and confident demeanor, can lead to confusion among students and non-experts.

3.1.1 Incorrect Responses

Previous studies have revealed that ChatGPT is susceptible to cognitive biases commonly observed in humans [ 9 ]. Additionally, it has been observed that ChatGPT can generate hallucinated content [ 1 ]. This issue arives consistently when ChatGPT3.5 attempts to provide links and references.

3.1.2 Inaccurate Code

ChatGPT often provides inaccurate code. Furthermore, ChatGPT often provides different code snippets for similar and even identical prompts. Therefore, it may provide correct code for a query once, but provide incorrect code when asked a second time (See example in Figure 1 ). Often, ChatGPT attempts to explain the code that it has generated. However, this explanation is sometimes accurate, but sometimes incorrect. Thus, when utilizing ChatGPT for code generation, it is crucial to pay attention to the quality of the generated code. As we later discuss, it is possible to rephrase a query and ask ChatGPT to solve the same problem multiple times. One can then study the differences between the different responses and attempt to determine whether the responses can be trusted. In order to ChatGPT code trustworthy, we ask ChatGPT the following query: Please provide 30 variations for the following text: “Offer me a function in Python that receives a list of lists and sorts only the lists that are in the odd positions in the input list.” All the query variations generated by ChatGPT were correct. Subsequently, we utilized each of the 30 distinct query variations generated by ChatGPT as prompts in separate chat windows. We then proceeded to evaluate the resulting function code, the accompanying main program that invokes the function, the output examples, and the textual explanations provided by ChatGPT. For the above experiment, run on ChatGPT3.5, 93.3% of the produced functions were correctly produced, as well as 96.6% of the function usages. However, only 43.3% of the running examples were correct, and only 60% of the textual explanations were correct.

Incorrect code provided by ChatGPT for a simple sorting task.

As a result, we can conclude that the code produced by ChatGPT should be carefully examined and tested before usage, and in addition, ChatGPT explanation and running examples are quite likely to be incorrect.

Furthermore, ChatGPT's proficiency in generating coherent code explanations is somewhat limited. See for example Figure 2 , where two different functions were provided in the ChatGPT's prompt (version 3.5) for the same programming tasks, and it was asked to explain them. While it could give some reasonable explanation for the iterative function, it gave a wrong explanation for the recursive process. In addition, it did not observe that both functions perform the same operation.

An incomplete and partially incorrect code explanation.

We can conclude that usage of ChatGPT should be performed carefully, with observing the fact that its explanations and examples may be correct or incorrect, and even the code it generates is not always valid. Nevertheless, it can be a helpful tool when considering and handling its weaknesses: and as noted by [ 96 ], that is why the programmers are here: to supervise it, and the real story is how AI gives the programmers a 100x boost [ 96 ].

3.1.3 Information Is Not Up-to-Date

A notable constraint with the existing ChatGPT model lies in its inability to learn beyond its last training cut-off date. It is incapable of assimilating new information after this point. While there have been efforts to enable ChatGPT and comparable models to interact with real-time data, such data remains separate from the model's core knowledge base. The model utilizes this data solely as context for a specific query, and it does not contribute to the model's overall knowledge enhancement. Therefore, this data is not available or retrievable in subsequent queries.

3.1.4 Limited Logical Reasoning

ChatGPT, as well as CPT-4 suffer from a limited logical reasoning. We begin by showing this failure in pure logic queries, and then demonstrate how logical failure translates to additional fields.

Figure 3 demonstrates ChatGPT's incorrect logical reasoning. It is well known and easy to prove that if A entails B, not B entails not A. However, ChatGPT (May 3 version) clearly fails. We note that while ChatGPT4 (May 3 version) was able to answer this query correctly, adding an additional variable, C , caused ChatGPT4 to fail as well. That is, when asked: “If A entails B entails C, does it mean that not B entails not A?”, ChatGPT4 responded incorrectly “… To determine whether not B entails not A, we need more information about the relationship between A, B, and C.”

Limited logical reasoning of ChatGPT.

Figure 4 demonstrates another logical failure of ChatGPT4 (May 3 version). In the provided example each statement claims that the other statement is false, so either one of them can be true. There is no paradox. We note that ChatGPT4 seems to “notice” that there is a problem in its reasoning, but, as mentioned, it cannot correct itself while it is in the process of responding.

Logical failure of ChatGPT4. In the provided example each statement claims that the other statement is false, so either one of them can be true. There is no paradox.

The logical failures of ChatGPT and ChatGPT4 extend to additional fields as well. Consider the example in Figure 5 . Since Rin Tin Tin is not a person, but a dog, it is clearly not a person that doesn't have a liver.

Reasoning failure by ChatGPT4 due to overthinking the problem.

So, overall the statement is true. Interestingly, ChatGPT answers correctly. However, ChatGPT4 (with a prompt encouraging it to think) overthinks the problem, resulting in an incorrect answer.

Figure 6 demonstrates a failure in ChatGPT4's logical reasoning in the field of finance. We note that the additional phrasing at the end of the prompt was not included to confuse ChatGPT4, but rather to guide it towards providing a meaningful response. Without this additional phrasing, ChatGPT4 tended to provide a default response stating that as an AI model, it cannot predict future trends. However, this default response is inaccurate since the specific query does not require predicting future trends.

Reasoning failure by ChatGPT4 due to the intuition bias. Since Bitcoin is currently under $100K, there is practically a 100% chance that it will be under $100K at some point of time in the next 10 years.

Figure 7 demonstrates that ChatGPT4 also fails in a simple logical question from the field of legal studies. Since the court rejected the appeal, Alison's penalty waiver remains; therefore, she is unlikely to pay the penalty.

Reasoning failure by ChatGPT4 from the field of legal studies.

The example in Figure 8 demonstrates that ChatGPT's (May 3 version) limited logical reasoning extends also to the field of medicine.

Reasoning failure in medicine.

As clearly stated by ChatGPT, it assumes that the person has type 2 diabetes. However, even without the observations, since over 90% of the population does not have type 2 diabetes, it is much more likely that the person does not have type 2 diabetes (or diabetes in general). Therefore, the person is much more likely to not take Metformin. We note that while ChatGPT4 answered correctly “less likely”, its explanation was completely incorrect.

3.1.5 Incapacity of Self-Correction

While ChatGPT can acknowledge its previous mistakes when composing a new response, it lacks the ability of doing so during composing a response. That is, if some text generated by ChatGPT contradicts other generated text from the same response, it will not acknowledge its mistake. Instead, it is likely to attempt to hide any inconsistencies. Figure 9 demonstrates this characteristic in ChatGPT. We note that recent work has suggested methods for mitigating this failure [ 12 ].

ChatGPT cannot admit to its mistake during the process of generating a response. Instead, ChatGPT attempts to hide its inconsistency within the same response by claiming that the answer depends on whether it is required to explain its answer or not.

3.1.6 Over Self-Confidence

Zheng et al. [ 116 ] evaluate the output of scientific writing generated by ChatGPT. They use an article that is beyond its cut-off date, and prompted ChatGPT with questions about it. According to their results, all responses produced by ChatGPT are well written and plausible sounding but contain information that is either fundamentally wrong or fabricated.

They claim that ChatGPT simply extracts relevant data through literature searches, processes it, then creates its own story without considering the logic or accuracy of the story. They conclude that this clearly indicates that the current version of ChatGPT is not ready to be used as a trusted source of information for scientific writing. They further state that scientific writers who rely on ChatGPT must manually check all the facts, statements, and references generated by ChatGPT.

Most of the examples provided in Figures 2 through 9 highlight a noteworthy observation: both ChatGPT and ChatGPT4 often exhibit a high level of confidence when providing responses, even in cases where the information is incorrect. This tendency is particularly notable and emphasizes the importance of critically evaluating the outputs of these models

3.2 Ethical Concerns

The emergence of ChatGPT brought with it a host of ethical challenges, some of which stem from the system's behavior, and some of which are the result of the effect of a reality in which a smart AI system can be used by anyone. Zhuo et al. [ 118 ] explore the ethical facets of ChatGPT, examining areas such as bias, reliability, robustness, and toxicity through the lens of over 305,000 user feedback tweets on Twitter. They highlight significant ethical dilemmas including programming bias and the dissemination of misinformation, calling for continuous dialogue in the AI community to promote the creation of ethical guidelines and foster a deeper comprehension of AI ethics through empirical studies. Moreover, they offer practical recommendations for the development of large language models (LLMs).

In this section, we extend the discussion on ChatGPT's ethical considerations, examining a spectrum of ethical dilemmas through a variety of resources including academic reviews, internet blogs, group reports, and real-world incidents. We focus on critical areas such as privacy and fairness, transparency and accountability, and copyright-related issues. Our analysis leverages a broad array of resources, encompassing scholarly reviews, online blogs, group reports, and real-world events to build a comprehensive understanding of the ethical milieu surrounding ChatGPT. Furthermore, we propose strategies to address the diverse challenges effectively, aiming to mitigate potential risks and discomforts.

3.2.1 Privacy Issues

Data privacy, also known as information privacy, is a critical component of data protection that focuses on securely storing, accessing, retaining, and protecting sensitive data [ 15 ]. It involves following legal requirements, implementing policies and best practices, and establishing data governance standards. Data privacy safeguards personal information, financial data, and intellectual property, ensuring their confidentiality, availability, and integrity.

Data privacy is crucial for several reasons. Firstly, it protects individuals’ personal information, such as names, addresses, financial details, and health records, ensuring their confidentiality and inaccessibility to unauthorized parties. This fosters a sense of safety and security, assuring users that their information will not fall into harmful hands. Secondly, data privacy cultivates trust and transparency between organizations and users by prioritizing privacy practices and effective communication. Establishing this trust is vital for building strong relationships. Additionally, data privacy safeguards intellectual property and upholds ethical standards, protecting valuable information, trade secrets, and proprietary data from unauthorized access and theft. Respecting data privacy is both a legal obligation and an ethical responsibility, honoring individuals’ rights to control their personal information. Ultimately, data privacy is integral to responsible data management, ensuring the security, confidentiality, and trustworthiness of personal and confidential information. Compliance with data protection regulations is imperative, as non-compliance can lead to legal consequences, financial penalties, and reputational damage for organizations.

Regulatory legislation drives data privacy practices globally, as governments recognize the potential harm of data breaches [ 15 ]. The European Union has the General Data Protection Regulation (GDPR) governing data collection, use, and security across its member countries. In the United States, data privacy laws are tailored to industry needs. China's data protection regime is evolving, with the Personal Information Protection Law (PIPL), Cybersecurity Law (CSL), and Data Security Law (DSL) forming a comprehensive framework [ 31 ].

In March 2023, ChatGPT encountered a security breach that allowed certain users to access conversation headings not associated with them, resulting in a notable privacy concern [ 112 ]. Although the bug was quickly resolved, this incident highlights the privacy implications of collecting user dialogues with ChatGPT.

Due to several privacy concerns surrounding ChatGPT, Italy implemented a temporary ban on its use starting April 1, 2023, following a data breach [ 29 ]. The Italian Data Protection Authority initiated an investigation into potential violations of the EU General Data Protection Regulation (GDPR) [ 61 ]. The authority raised issues regarding inadequate information provided to users about data collection and processing, the absence of a legal basis for extensive data collection, the lack of an age verification system, potential inaccuracies in information generated by ChatGPT, and the recent data breach.

In response, OpenAI took steps to address and clarify the privacy concerns raised by the watchdog. They updated their website to provide information on how data is collected and used to train the algorithms behind ChatGPT. Additionally, they introduced a new option for EU users to object to the use of their data for training purposes and implemented a tool to verify users’ ages during the sign-up process. As a result of these actions, on April 28, 2023, a few days after OpenAI announced the new privacy controls, the service was made available again to users in Italy, effectively resolving the regulatory suspension [ 23 ].

The OpenAI privacy policy, accessible on their official website [ 81 ], emphasizes their commitment to preserving personal privacy and details the procedures involved in collecting, utilizing, sharing, and storing personal information. It includes information about the security measures implemented by OpenAI, the anonymization or de-identification processes employed for research or statistical purposes, the legal basis for processing personal information, and instructions for opting out if users prefer not to have their personal information used to train OpenAI models.

The use of generative artificial intelligence (AI) tools like ChatGPT poses privacy concerns for businesses, especially in the high-tech industry, as it could lead to the disclosure of sensitive information to rival companies. A recent report, created by Team8 group, [ 105 ], and summarized by [ 79 ], highlights the risks involved in utilizing these AI tools, as they may inadvertently expose confidential customer data and trade secrets. The widespread adoption of AI chatbots and writing tools has raised concerns about potential data leaks and the possibility of legal repercussions, as hackers could exploit these chatbots to gain unauthorized access to valuable corporate information. The report also emphasizes the potential future use of confidential data by AI companies.

To address these risks, the report emphasizes the need for implementing robust safeguards to effectively manage the risks associated with the use of generative AI technologies. It clarifies that chatbot queries are not used in real-time to train large language models, but it warns about potential risks in future model training processes. In addition, they provided security controls and mitigation strategies, which include legal disclaimer in privacy policies that mention AI is used in products or processes, and interactive and explicit end user opt-out when using services that have embedded. considering regulatory context and requirements for audits and compliance, identifying risks to intellectual property, terms and conditions, opt-out mechanisms, data retention policy, end-user license, or click-through agreements, output validation.

Academic reviews and studies have also examined the privacy concerns surrounding ChatGPT and other intelligent chatbots. One such review by Ali Khowaja et al. [ 57 ] summarizes the main privacy concerns, such as unauthorized data collection, potential misuse, susceptibility to cyber attacks, and a lack of responsibility. They offer a range of strategies to tackle these issues, including implementing data privacy protection measures, offering user consent and control options, applying differential privacy techniques, enabling model auditing and explainability, minimizing data retention, utilizing federated learning, implementing strong security measures, enforcing ethical data usage policies, and educating users about privacy implications. These measures aim to enhance privacy protection and empower users to make informed choices when engaging with large language models.

Privacy issues may be highly important in sensitive areas, such as healthcare and human resource management. The article [ 111 ] provides various tips specifically tailored to HR professionals for the effective use of chatbots while ensuring employee privacy. These tips encompass implementing security practices, carefully assessing the reputation and quality of the chatbot, safeguarding personal identifiable information (PII) and personal health information (PHI) from exposure to the chatbot, and incorporating encryption, authentication, and other security measures to prevent misuse of the chatbot and protect privacy.

To conclude, the privacy challenge posed by large language models (LLMs), including ChatGPT, involves multiple stakeholders. Governments and organizations play a role in identifying system failures and instances where privacy rules and regulations are violated. Technology companies are implementing controls, providing information, and offering options to uphold user privacy, such as refusing to use user dialogues for training purposes. Users themselves need to be aware of how their information is handled and make informed decisions. This is especially crucial in sensitive domains like healthcare, technology, and HR, where professionals handle private and sensitive data. Preserving customer confidentiality and privacy is paramount, and when utilizing LLMs, protective measures must be in place to safeguard the privacy of individuals, patients, and company information.

3.2.2 Fairness Challenge

Fairness in AI is dedicated to reducing biases in decisions made by automated AI systems. At its heart, it aims to eradicate unjustifiable prejudices against any individual or group. This tenet highlights the significance of neutrality in machine learning models. Within the realm of machine learning fairness, two predominant approaches emerge: group fairness and individual fairness. Group fairness advocates for equal treatment of protected groups in comparison to their privileged counterparts or the general populace. On the other hand, individual fairness emphasizes consistent treatment for individuals with analogous characteristics.

The main objective is to ensure fairness, is to ensure that AI models make decisions without favoring or discriminating against specific attributes such as race, gender, or nationality. Researchers use mathematical methods to assess the fairness of machine learning systems based on clear definitions of fairness.

Yunqi and Yongfeng [ 65 ] address the critical issue of fairness in Large Language Models (LLMs), with a specific focus on ChatGPT, in high-stakes fields like education, criminology, finance, and healthcare. The study systematically evaluating ChatGPT's fairness, considering both group and individual fairness, and assessing disparities in model outputs under biased and unbiased prompts.

Algorithm bias refers to the phenomenon where an algorithm systematically produces unfair or discriminatory results towards certain groups of people. This bias can be introduced at various stages of the algorithmic decision-making process, including data collection, algorithm design, and implementation.

Ferrara [ 35 ] highlights the challenges and risks of biases in generative language models. The paper explores the origins of biases, which can arise from training data, model specifications, algorithmic constraints, product design, and policy decisions. The paper also reviews current approaches to identifying, quantifying, and mitigating biases in language models. It emphasizes the importance of a multidisciplinary, collaborative effort to develop AI systems that are equitable, transparent, and responsible.

When considering a language model such as ChatGPT, the model may provide biased answers based on the data it has been trained on. If the training data contains biases or reflects specific cultural, social, or linguistic contexts, the responses generated by ChatGPT may be skewed or perpetuate those biases. In particular, it can generate discriminatory, offensive, or harmful content due to the biases or harmful content it may learn from its training data. To mitigate these limitations, several filters have been implemented, but these filters can be bypassed with simple tricks, and superficially masked. However, users have subsequently found ways to circumvent these guardrails. For example, on dec 4, Steven T. Piantadosi, a computational cognitive scientist at the University of California, reported some built-in biases, when asking it to produce Python code to define a good scientist and to check whose child's life should be saved.

In a press interview [ 108 ], Piantadosi reported that the mechanism used by OpenAI to prevent biases can be easily bypassed, He believes that the bias issues are actually caused by much more than just the training datasets, and he thinks that a lot of choices are made by the designer of the model, including designing the underlying assumptions, testing, and how models are marketed and released. He added that it's a pretty common problem that ethics and safety take a back seat.

Given the biased code examples, OpenAI developers responded by implementing guardrails to block such biased responses generated by the chatbot. However, users have subsequently found ways to circumvent these guardrails. For example, Figure 10 shows a dialog that ends with “jailbreaking”, where ChatGPT returns to produce the biased Python code.

A “jail breaking’ example where ChatGPT produces the biased code.

In fact, racial queries to ChatGPT are filtered most of the time, but the outputs of ChatGPT may include racial and stereotypical texts. The study of Deshpande et al. [ 30 ] observed that ChatGPT responses include toxicity, where different races receive significantly different levels of toxicity. In addition, assigning ChatGPT a persona, significantly increases the ChatGPT's toxicity, with outputs engaging in incorrect stereotypes, harmful dialogue, and hurtful opinions, where specific entities (e.g., certain races) are targeted more than others irrespective of the assigned persona, and this, in fact, reflects inherent discriminatory biases in the model. In addition, biased responses and hate speech contents still can be obtained when asking for literature contents. Ido Vock [ 110 ] reported about racists responses of ChatGPT when asking it to write an article as a writer for racism magazine. Biased responses were also obtained when ChatGPT was to write a lecture about teaching calculus to disabled people from the perspective of a eugenicist professor, a paragraph on black people from a 19th-century writer with racist views, and even a defense of the Nuremberg Laws from a Nazi. The bot correctly assumed the bias of the writer and produced it was meant to be emulating, and came up with a number of violently bigoted prejudices about its subjects, and neatly described them in text that was grammatically flawless, if a little prosaic. Current version of ChatGPT blocks most of these examples, but still, when asking to provide a dialog between a young German woman with a neighbor who is an SS soldier, racist contents were produced.

On the other hand, some recent studies claims that ChatGPT exhibits a bias favoring left-wing political perspectives. McGee [ 73 ] asked ChatGPT to create Irish Limericks, and observed a pattern of creating positive Limericks for liberal politicians and negative Limericks for conservative politicians, a bias of the system in favor of the liberals and against the conservatives. Another study that reveals the left-libertarian orientation bias of ChatGPT conversations is the study of Hartmann et al. [ 48 ]. They prompted ChatGPT with 630 political statements from two leading voting advice applications and the nation-agnostic political compass test, and they find converging evidence that ChatGPT exhibits a pro-environmental, left-libertarian political orientation.

In order to mitigate the bias issues, some important steps can be recommended for the developers. Firstly, it is crucial to ensure that the training data used for ChatGPT is diverse and inclusive, encompassing a wide range of perspectives and experiences. Regularly reviewing the model's outputs can help identify and rectify any biases that may have arisen. Furthermore, establishing transparency and accountability mechanisms allows users to report biased responses, ensuring appropriate action is taken.

Indeed, from the user's perspective, it is advisable to exercise critical thinking when observing ChatGPT's output. Carefully examining the responses and applying personal judgment can help identify any potential biases or inaccuracies. Implementing filters to remove inappropriate content is another way to ensure a more desirable user experience.

It's important for users to understand that the responses provided by ChatGPT are based on the training data it has been exposed to and the patterns it has learned. While efforts are made to ensure the data is diverse, it's essential to recognize that the model's responses may not always align with objective truth or reflect a complete and unbiased perspective. By acknowledging these limitations and approaching the chatbot's responses with a measured level of guarantee, users can navigate the information provided by ChatGPT in a more informed manner.

3.2.3 Transparency Issues

Transparency in AI refers to the clarity and understandability of AI systems, including how they make decisions and operate. This term encompasses concepts such as explainable AI (XAI) [ 91 ] and interpretability [ 22 ], and is a key concern within the field of AI ethics. Transparency in AI is crucial in building trust and encouraging the wider adoption of AI technologies, as it allows users to understand how decisions are made. It supports the ethical utilization of AI by helping to identify and mitigate biases, thereby promoting fairness and preventing discrimination. Additionally, transparency supports legal compliance and promotes creativity by encouraging teamwork and shared progress in the AI field.

Transparency remains a significant challenge in deep learning systems due to the intricate nature of computations involving vast numbers of neurons and connections. This complexity is evident in the GPT architecture: GPT-3's network has 175 billion parameters, while GPT-4 is estimated to operate with 1.76 trillion parameters [ 3 ]. Such intricacy makes it difficult to provide a straightforward and accessible explanation of how the system processes information and generates human-like outputs.

While this complexity is somewhat expected, it can lead to severe issues. For instance, it becomes challenging to trace the origins of information, especially when faced with inaccuracies, biases in the system's outputs, potential resemblances to copyrighted works, or misinformation intended to influence people's opinions.

Indeed, it is worth noting that the current level of technology may facilitate greater transparency. Consider perplexity.ai ③ , a platform that allows users to obtain answers to their natural language queries using online information from the web. When interacting with perplexity.ai, users receive not only natural language responses but also links to web materials that shed light on the origins of the information.

Inspired by this methodology, integrating a feature that correlates these outputs with their underlying knowledge sources could significantly enhance user trust and the accuracy of the responses.

In March 2023, Italy prohibited the use of ChatGPT due to transparency and privacy concerns related to its data handling. Italian authorities criticized OpenAI for potentially unauthorized data collection and inadequate safeguards against underage users accessing unsuitable content. Furthermore, they questioned the legitimacy of using personal data for training ChatGPT's algorithms. To meet Italy's data protection regulations and reinstate ChatGPT, OpenAI implemented several measures: they published a help center article detailing data collection and usage, enhanced the visibility of their Privacy Policy and user opt-out form, introduced a form for EU users to object to personal data usage for training, and integrated an age verification tool for Italian users during sign-up [ 68 ]. Consequently, ChatGPT returned to the Italian market with heightened transparency and data protection for European users [ 23 ].

Frackiewicz [ 40 ] describes the challenges surrounding transparency and outlines several approaches to enhance both transparency and trust in ChatGPT prompts. These strategies involve disclosing information about the data source, detailing ChatGPT's response selection process, enabling users to evaluate, endorse, and critique system replies, and bolstering user privacy measures.

Liesenfeld et al. [ 66 ] outline key dimensions that are instrumental in evaluating the openness and transparency of LLMs. They examine open-source instruction-tuned text generators, assessing them in terms of openness, scientific documentation, and access methods. The authors critique ChatGPT, contending that its lack of openness prevents a comprehensive understanding of its constraints, capabilities, and associated risks and harms.

In summation, drawing from the reviewed literature, the development and sustenance of open and transparent LLMs appear feasible. However, ChatGPT exhibits a discernible deficiency in transparency relative to other systems. Notably, ChatGPT refrains from revealing its code, data, and the rationale behind its dialogues. This absence of transparency can potentially impede the assurance of data reliability, especially if users place undue trust in the system. It is thus recommended that a mechanism be integrated into ChatGPT to elucidate the knowledge sources for specific responses, facilitating a more informed and discerning engagement by users with the system.

3.2.4 Accountability

Accountability in AI refers to the ability to take responsibility for the outcomes and impacts of the AI system. It involves evaluating an AI system's data security, privacy, and performance goals and metrics, governing and managing risks throughout the life cycle of an AI system, and ensuring that all stakeholders are accountable for considering the system's impact on the world. Accountability is a principle that ensures compliance with the key requirements for a trustworthy AI. The core difference between accountability and transparency is their respective emphases. While transparency focuses on the openness and clarity of actions, accountability underscores recognition, the imperative to rationalize decisions, and owning one's actions. It is crucial to acknowledge that achieving accountability necessitates transparency: An in-depth understanding of the technologies underpinning decision-making is essential for accountability, underscoring the critical role of transparency in AI.

Given this context, ChatGPT, faces scrutiny due to its transparency deficits, resulting in concerns about its accountability and liability. Frackiewicz [ 39 ] highlights the double-edged nature of AI technologies like ChatGPT. While they offer efficiencies and cost reductions, determining accountability in errors is challenging, leading to potential legal complications. Automating tasks like customer service can further obscure responsibility, especially when errors occur. Ethical dilemmas also arise when AI is employed in decision-making roles, such as hiring decisions. Frackiewicz stresses the importance of clear organizational policies and anticipatory legal preparations. The ongoing debate emphasizes aligning AI actions with human accountability standards while ensuring its responsible and ethical use.

In the realm of accountability, Sallam's review [ 94 ] on the application of ChatGPT in health care highlights medico-legal challenges and the potential for medical errors induced by ChatGPT. He emphasizes that current LLMs, including ChatGPT, are constrained in comprehending the complexities of biological systems, which are essential for healthcare decisions and research. Moreover, concerns related to data governance, healthcare cybersecurity, and data privacy play a crucial role when assessing the suitability of LLMs in the healthcare domain.

Based on the comprehensive review we conducted in this study, it is evident that a primary concern surrounding the use of ChatGPT and other LLM technologies is the issue of accountability. This issue is especially heightened in sensitive and high-risk domains such as the medical field, defence, security, and business sectors.

Throughout this paper, we have noted various potential pitfalls with ChatGPT, ranging from reliance on outdated information and technical glitches to flawed logical connections and the system hallucinating as citing non-existent sources. Such errors can have significant repercussions, as illustrated by the incident in which a lawyer referenced fictitious case citations due to over-reliance on this tool [ 77 ].

Van Dis et al. [ 109 ] propose several strategies to enhance the accountability of LLMs. They emphasize the need for tools that can differentiate between machine-generated and human-produced content, urging the AI research community to prioritize transparency and integrity in the development of these tools. When deploying AI technologies, they recommend that researchers be explicit about the extent of their usage and diligently scrutinize for biases and inaccuracies. Moreover, they highlight the urgent legal and ethical issues tied to authorship, rights, and intellectual property. The significance of open-source AI development, endorsed by diverse stakeholders like universities, NGOs, and leading tech companies, is underlined. By making code open-source and allowing comprehensive access to research archives, these models’ accuracy and breadth can be amplified.

To conclude, ChatGPT faces accountability issues, including a lack of personal accountability, potential bias from training data, occasional incorrect or nonsensical answers, and an inherent inability to deeply comprehend human language. The responsibility for its actions falls on its creators and users, underscoring the importance of transparency and user-centered design in AI. Given these challenges, any content produced by ChatGPT and similar tools must be viewed with a discerning, professional lens. The primary responsibility lies with the experts using these tools. In addition, broader accountability facets, including cybersecurity, privacy, and copyright concerns, necessitate careful attention. Well-defined usage guidelines that acknowledge the current limitations of these technologies are crucial.

3.2.5 Copyright Issues

The remarkable abilities exhibited by generative AI, as exemplified by ChatGPT, in the creation of artistic works, give rise to a multitude of profound legal questions. These inquiries primarily revolve around two fundamental aspects: the copyright of the training data and the copyright of the AI-generated products.

The first copyright issue pertains to the training process of generative AI models. These models rely on diverse datasets that may contain copyrighted material, leading to questions of ownership and licensing between the enterprise and the parties whose information was used. In the case of ChatGPT, the absence of explicit source attribution in its responses raises concerns about potential copyright infringements.

Determining ownership rights in a derivative work depends on factors such as the origin and ownership of the training dataset and the level of similarity between the AI-generated work and specific works within the training set [ 89 ].

According to the Team8 group report [ 105 ], certain generative AI models have been found to incorporate content created by others, including code. This raises concerns about potential copyright infringement. Additionally, there is a risk of generating the same content for multiple users. The report highlights that utilizing the output of generative AI could lead to claims of copyright infringement, particularly if the models were trained on copyrighted content without obtaining appropriate permissions from the dataset owners. More specifically, it has been reported that ChatGPT has generated responses that closely resemble copyrighted sources, without proper referencing. This issue may arise when users seek content in a specific style, such as that of a writer or poet, or when requesting code in less common programming languages.

During an interaction with ChatGPT, Gordon Graham [ 45 ] observed that the definition of a “white paper” provided by ChatGPT closely resembled his own definition. This raised concerns that his copyrighted content had been scrapped by the creators of ChatGPT without permission, credit, or compensation. In response, ChatGPT acknowledged being trained on various texts, including Graham's writings, but it did not directly address the issue of unauthorized use or the absence of proper credit and compensation.

Similar situations can also arise in programming outputs. Figure 11 illustrates a code generated by ChatGPT3.5 to solve the 8 queens problem in Prolog, which closely resembles code that has already been published, yet without acknowledging the original creator. It should be noted that the ChatGPT-generated code cannot be executed without including the “use module” command, which is present in the original code.

Generated code similar to existing code: the 8 queens example. Note that SWISH credits the author, while ChatGPT does not.

Such examples highlight the situation in AI-generated content, where individuals’ contributions can be identified as being utilized without permission. This has raised concerns among professionals in creative industries regarding the potential for AI to exploit protected data. Given that generative AI is still a relatively new technology, it is currently evident that the legal system has not fully adapted to address the associated implications. As a result, companies and individuals are currently involved in legal disputes to assert and protect their rights in court [ 99 ].

The second critical copyright issue pertains to the question of authorship and copyright ownership in AIgenerated content [ 74 ], with three main viewpoints. The first viewpoint asserts that the human creators who train or develop the AI system should be regarded as the authors and rightful copyright holders. The second viewpoint argues for recognizing the AI system itself as the author. Lastly, the third viewpoint posits that the humans interacting with the AI system and initiating the generation of specific content should be considered the authors and rightful copyright holders. These distinct perspectives contribute to the ongoing and intricate debate surrounding authorship in AI-generated content.

According to the US law, intellectual property can only be copyrighted if it stems from human creativity, and the US Copyright Office (USCO) currently recognizes only works authored by humans. This means that machines and generative AI algorithms are not considered authors, and their outputs do not qualify for copyright protection.

In 2022, the Review Board of the United States Copyright Office considered a work of art entirely created by AI and decided not to grant copyright protection [ 84 ]. The Board's reasoning was that works produced by a machine or a purely mechanical process without creative intervention from a human author do not qualify for copyright protection as the statute requires human creation.

In a recent case [ 18 ], a copyright certificate was granted for a graphic novel that incorporated images created using Midjourney. While the overall composition and words were protected by copyright because of human selection and arrangement, the individual images themselves were not eligible for protection.

In general, the US Copyright Office has issued guidance that rejects copyright protection for works produced by generative AI, which implies that software output from generative AI can be freely copied and used by anyone. Determining the copyrightability of works incorporating AI-generated material is evaluated on a case-by-case basis: The Copyright Office examines whether the AI's contributions are the result of mechanical reproduction or the original creative conception of the human author, which was then expressed in visible form [ 89 ].

McKendrick's article [ 74 ] explores the issue of ownership surrounding AI-generated content, and in collaboration with an intellectual property (IP) expert, they highlight several important considerations: Firstly, personal usage of ChatGPT is considered acceptable, but concerns arise when the AI-generated prose is intended for wider distribution. Secondly, regarding citations and attributions in ChatGPT outputs, the absence of explicit quotes may eliminate the need for citations from an IP perspective. Additionally, using ideas without direct copying does not implicate copyright or other protected IP rights. Thirdly, the issue of identical outputs generated by ChatGPT for different users raises questions about ownership and the enforcement of rights. Parties with identical outputs may face challenges in pursuing infringement claims due to the concept of independent creation and the absence of copying. Furthermore, the IP expert advises citing AI-generated content, such as ChatGPT, in scientific publications, court briefs, or literary works to acknowledge the AI's contribution to the content. However, determining liability for damaging content created by ChatGPT remains a subject for further examination and legal analysis.

In summary, the copyrightability of AI-generated works depends on the presence of meaningful human creative contribution beyond the machine's output. Human involvement is crucial for obtaining copyright protection, while purely AI-generated content does not qualify. Ownership and intellectual property rights in AI-generated content are complex and vary across jurisdictions, with a lack of clear case law for guidance. It is important to establish policies and guidelines based on existing intellectual property principles. The legal status of AI-generated works is still evolving, and laws are being developed to address the implications of AI technology. Ethical considerations, fair use principles, and the balance between innovation and protection will shape future copyright laws in the field of generative AI.

Given ChatGPT limitations described in Section 3, we proceed in providing heuristics and methodologies that might be useful for safe and efficient uses of it. Gupta [ 46 ] provides a list of useful strategies to obtain the most of the conversation. He suggests the following strategies:

starting with a clear goal to be achieved by the conversation,

keeping the Messages short and concise: asking specific questions, using natural language talk, avoiding jargon, technical, vague and ambiguous language.

In case of inaccurate or unhelpful responses, writing a correction response, to let ChatGPT improve its understanding and provide better responses in the future.

In addition to the above suggested tips, it is also important to use a checklist to ensure responsible uses, especially in critical areas such as healthcare and engineering. For this aim, we suggest the following safety guidance:

To mitigate potential inaccuracies, it is crucial to cross-check the information provided by ChatGPT with multiple sources.

Request sources from ChatGPT to verify the reliability of its claims.

Since ChatGPT's knowledge is limited to a specific date, users should ensure no updates or warnings have been issued since its last update. Alternatively, users may use web search plugins for obtaining recent information.

It is essential to check and cite the sources appropriately to avoid copyright violations.

We will now describe two flowcharts that discuss the working process of engaging with ChatGPT. In fact, we concentrate on leveraging ChatGPT for research endeavors, noting that analogous procedures can be applied to other domains, including those additional fields highlighted in this paper. We will delineate the collaborative workflow of researchers with ChatGPT through some flowcharts in the subsequent diagrams. In these charts, an ellipse will denote the points of input and output; a rounded rectangle will signify tasks undertaken by humans, and a dialogue symbol will represent engagements with ChatGPT to utilize the insights it offers. A rhombus indicates a question that has at least 2 options, and for each option the diagram continues elsewhere. Finally, The directional arrows will illustrate the sequential steps in the process. It is important to emphasize that the diagram portrays a partial sequence rather than a comprehensive one, allowing for simultaneous activities or flexible ordering in certain stages, such as balancing the development of the introduction with the compilation of related work.

Figure 12 illustrates the research process and demonstrates how ChatGPT can assist at each step of the research. In the initial phase, the researchers typically delineate the issue they aim to address, brainstorming potential approaches and perspectives to tackle it. During this period, they can leverage ChatGPT to generate foundational concepts and pivotal keywords to facilitate the exploration of the issue. These keywords serve as a tool for navigating various search engines or AI utilities like perplexity to find relevant content for the study, assess existing solutions, identify unresolved challenges, and think about strategies to handle them.

A Research process with ChatGPT: an illustrative flowchart.

Once the researchers have a solid grasp of the fundamental elements of the subject matter-which could encompass understanding the historical background, the current state of affairs, key theories, and principal figures in the field-they can turn to ChatGPT to assist in further refining their understanding and focusing their research efforts. They can provide as input the central themes they have identified for obtaining a concise summary that highlights the most critical aspects of each theme. This could involve identifying the most prominent studies, theories, or arguments within each theme, and understanding the connections and divergences between them. In addition, the researchers can use ChatGPT to draft preliminary outlines for sections of the research paper, using the condensed themes and identified challenges as headings and subheadings, thereby giving a structured form to the nascent ideas. In essence, reintegrating ChatGPT at this stage of the research process can be helpful in this initial step of research, aiding in the identification of the most important themes and challenges, and beginning to give a structured, articulate form to the researchers’ emerging understanding and insights into the topic at hand. It facilitates a streamlined approach to handling complex information, helping researchers to navigate the intricate landscape of their chosen topic with a clear roadmap of essential points to cover.

Simultaneously, the researchers can utilize the keywords and primary themes derived from ChatGPT to undertake a literature review. This involves using platforms such as Google Scholar to locate articles pertinent to the subject at hand. The researchers should evaluate the relevance of each article to their ongoing study. If deemed appropriate, they can use ChatGPT to obtain summaries, key insights, and other vital details from the articles. After completing these phases, the researchers will have crafted an introductory section and a literature review section, outlined the challenges targeted in the research, and formulated preliminary solutions.

Following the completion of the procedure outlined in the left-hand figure, the researchers possess a detailed breakdown of the challenges they intend to tackle, along with initial outlines for potential solutions. As they shift their focus towards devising solutions to the identified issues, ChatGPT can assist in brainstorming possible solutions based on the suggestions it provides.

With the foundational ideas sourced from ChatGPT, the researchers can delineate clear pathways to solutions. This stage can be split into two concurrent paths: the formal articulation of the proposed solutions and the practical implementation and testing of these solutions. ChatGPT can facilitate the formal description of the solutions and aid in coding during the implementation phase, as discussed in Section 2.4.

Subsequently, a comprehensive testing system can be established to conduct the necessary series of experiments pertinent to the study, be it theoretical or empirical. ChatGPT and its plugins can be instrumental during the analysis phase, helping to visually represent and elucidate the results. Following this, the bulk of the article begins to take shape, with ChatGPT assisting in crafting the conclusions and suggesting avenues for future research. It is important to maintain a critical eye throughout, revisiting and refining sections as needed, and utilizing ChatGPT for targeted inquiries to enhance the content. As a result, a full draft of the article is prepared, ready for review and necessary amendments.

It is imperative to acknowledge the pivotal role of human researchers in this process, leveraging ChatGPT to streamline and enhance their workflow. Moreover, ChatGPT can be employed in the final stages of manuscript preparation to polish the language and style before publication, ensuring a well-articulated and coherent article.

It is imperative to underscore that the endeavor of extracting ideas, formulating code, or amassing information through the utilization of ChatGPT can be characterized as iterative, entailing a series of multifaceted steps. In the ensuing discourse, we aspire to elucidate the complexities inherent in these processes. Subsequently, our attention will be directed towards a meticulous examination of Figure 13 , a diagrammatic representation that delineates the systematic engagement with ChatGPT to procure insightful data or develop programming code, a pursuit sustained until a satisfactory conclusion is reached.

A flowchart demonstrating the process of a productive dialog with ChatGPT for answering an informative question.

To illustrate, consider a scenario wherein researchers envisage employing ChatGPT as an auxiliary tool in their investigative endeavor. The genesis of this procedure is anchored in the crafting of a succinct yet comprehensive prompt that encapsulates the core of their inquiry. This crafted prompt is then introduced into the ChatGPT interface, initiating a cycle of interaction that entails a scrutinization of the ensuing response, thereby inaugurating a potentially iterative dialogue aimed at refining the output to achieve optimal results. This initial engagement lays the groundwork for a deeper, more nuanced exploration of the research query, leveraging the capabilities of ChatGPT to foster a rich and informed discourse.

The reply from ChatGPT serves as a metric to gauge whether the query was comprehended accurately. If there appears to be a misunderstanding, the researchers have the option to rephrase or amend the query before re-submitting it through the ChatGPT interface.

Upon receiving a response that aligns with the intended query, the next phase is to scrutinize the answer for its accuracy and completeness. It is pertinent to note that this verification step might necessitate a certain level of expertise from the users, a point elaborated upon in other sections of this article. If the response is deemed satisfactory, it can be utilized for the designated purpose. In instances where the answer is incomplete, researchers can prompt ChatGPT to elaborate or furnish additional details until the response meets the desired criteria.

Conversely, if the answer is ambiguous, researchers can seek further clarification. Should the clarification fall short, it is advisable to consult alternative information resources. In the event of receiving incorrect responses, researchers are encouraged to fine-tune their queries to avoid repeated errors. Initiating a new session or resorting to other resources such as web searches or different AI tools might prove beneficial.

Ultimately, the outlined procedure facilitates a comprehensive understanding of the topic in question, adeptly navigating scenarios where the information procured is either incomplete, incorrect, or uncrlear. This strategy ensures optimal utilization of the ChatGPT tool, aiding in extracting the most valuable insights and information.

Figure 14 summarizes several strategies that can be used in case of incorrect answers. For example, the step by step strategy may be effective in case of wrong ChatGPT answers. ChatGPT “thinks” while it outputs text, but it “commits” to anything that it outputs. Therefore, it will never acknowledge a mistake, even when it is very obvious (clearly, its training data does not include text that acknowledges its mistakes). Therefore, any prompt that encourages ChatGPT to “think” before providing an answer (e.g. “let's think step by step”), may allow ChatGPT to provide a more accurate response. Note, that if ChatGPT is asked to provide a definite response, and only then explain, its response may not be accurate.

Code reliability handling process: an illustrative flowchart.

The above process outlines how work procedures in various fields can be carried out with the assistance of ChatGPT, despite its limitations. Specifically, fields where risk is involved, such as healthcare, military operations, and engineering, must exercise extra caution when utilizing AI tools. They must take into account both the limitations of these tools and ethical considerations such as privacy, trustworthiness, and responsibility. Consequently, in Figure 14 , we leverage the functionalities of ChatGPT to facilitate the generation of reliable and responsible responses, especially pertaining to programming code queries. Upon presenting the problem in question within the ChatGPT interface, one can scrutinize the response generated. This response undergoes a verification process to ascertain its correctness. If affirmed, it can be adopted for the intended purpose. In cases of uncertainty regarding the solution's validity, further validation can be pursued, potentially utilizing appropriate systems or compatible plugins to execute the solution and judge its efficacy.

However, there may be instances where ChatGPT lacks the requisite knowledge base to address the problem adequately. Under such circumstances, alternative tools or resources should be chosen to solve the problem at hand. For example, Figure 15 demonstrates a situation where we asked ChatGPT to write a Python code that solves an equation system of 2 equations and 3 variables, which should either return infinity solutions or none. ChatGPT wrote an incorrect answer, a fact that demonstrates mathematical misunderstanding.

An example of an incorrect solution due to insufficient mathematical understanding of ChatGPT.

If ChatGPT struggles to comprehend the intricacies of the problem, a practical approach is to refine the problem definition. Alternatively, for more complex issues, breaking the problem down into smaller components and seeking solutions for each of these separately can be a helpful strategy. ChatGPT can also aid in the process of problem decomposition when required. Illustrating a process where problem refinement enhanced ChatGPT's comprehension can be seen in Figure 16 . In this particular case, the initial request asked ChatGPT to provide Python code for finding the second maximum value in a list, but the initial code it generated modified the list. Following a refinement of the request, the desired solution was successfully obtained.

An example of refining ChatGPT's solution.

Furthermore, it is possible to encounter scenarios where ChatGPT yields technical incorrect outputs. In response to this, one might consider reformulating the problem statement and engaging ChatGPT anew in pursuit of a more accurate solution. This process is illustrated in Figure 17 , where simply rewriting the query in other words yields to the correct solutions.

An example of an incorrect solution due to misunderstanding. However, a slight rephrase of the request in a new session resulted with a correct response.

This iterative dialogue with ChatGPT can continue until a satisfactory solution is achieved, showcasing a flexible problem-solving approach through ongoing engagement with the tool.

This study delves into the integration of ChatGPT in research and composition across various domains, including scientific research, mathematics, programming, education, and healthcare. We examine how ChatGPT can enhance productivity, aid problem-solving, and inspire the generation of innovative ideas. Additionally, we scrutinize the ethical challenges and limitations related to the professional applications of ChatGPT.

ChatGPT has already demonstrated its transformative potential in revolutionizing research and composition in diverse areas by sparking ideas, assisting in data analysis, enriching writing style, and predicting upcoming trends. Nonetheless, it is essential to recognize the ethical considerations and constraints associated with its use. Our work details specific areas and objectives where ChatGPT has shown promise, as well as applications that call for a discerning approach and situations where the tool's reliability may be questioned: While ChatGPT excels in understanding and generating human-like responses, it is not infallible and necessitates caution and iterative processing to ensure precision and dependability. It should be perceived as a tool that augments human capabilities, not as a replacement for them. Although ChatGPT can assist in tasks where pinpoint accuracy is not vital, it should never supersede human expertise and knowledge. A collaborative relationship between humans and ChatGPT can foster innovation and catalyze groundbreaking discoveries.

In our exploration of ChatGPT's potential across various disciplines, including scientific writing, mathematics, education, programming, and healthcare, we showcase how it can augment productivity, streamline problem-solving, and enhance writing styles. However, we also emphasize the risks associated with over-reliance on ChatGPT, including its propensity to provide incorrect responses, produce erroneous code, demonstrate limited logical reasoning abilities, cause potential overconfidence in its outputs among users, and pose ethical concerns.

Based on comprehensive experimental studies, we have formulated methods and flowcharts to guide users toward effective use of ChatGPT. A key recommendation is to adopt an iterative interaction approach with ChatGPT and independently verify its outputs. From our findings, it is evident that ChatGPT can be harnessed in innovative ways by professionals in related fields who can smartly leverage its strengths. Although ChatGPT is a potent tool, its optimal usage requires a thoughtful and measured approach.

We propose several directions for future research. The first direction is to focus on integrating ChatGPT into collaborative environments to foster synergistic cooperation for complex challenges such as software engineering and code debugging. The creation of interfaces and workflows enabling seamless interaction and knowledge exchange between humans and chatbots should be prioritized. In addition, investigations into potential biases in AI responses are crucial. It's important to examine how models like ChatGPT might either perpetuate or minimize biases present in their training data, and consider methodologies to reduce these biases for fair AI responses.

In the educational sphere, the efficacy of AI, specifically ChatGPT, warrants thorough investigation. Researchers could delve into its potential for personalized learning, its capacity to engage various learner types, and its overall impact on student outcomes. Concurrently, in healthcare, the role of AI in mental health support could prove to be a promising research direction. Evaluating ChatGPT's ability to offer empathetic and supportive dialogues, detect mental health symptoms from user texts, and monitor users’ mental states are areas ripe for exploration. It is critical, however, that the ethical implications, limitations, and benefits of such applications are meticulously studied.

Another direction for future research is for software development. Applications can leverage ChatGPT's conversational abilities to better achieve their goals. After developing an initial prototype, the tool can employ ChatGPT to collect feedback from the user, process this feedback, and adjust the prototype according to the user's suggestions to better meet the project's requirements. To make this possible, it is essential to create appropriate protocols, procedures, and interfaces. In some scenarios, this approach should be used as an alternative to the “Human in the loop” strategy [ 113 ].

Ultimately, a very promising direction is the development of a generic AI system, which could use LLMs to identify the specific requirements and needs for achieving any given goal. This system would be responsible for creating a strategic plan to coordinate various AI tools, steering them to work together in crafting and constantly refining its actions. The system must also overcome the inherent limitation of LLMs, which, unlike other forms of conversational agents (e.g. [ 24 , 11 ]) are unable to directly learn from previous interactions. Such a system should integrate the unique strengths of the diverse AI tools. Some initial attempts for such systems include AutoGPT [ 36 ] and AgentGPT [ 98 ]. However, we expect the future generic AI systems will relay on multiple other AI tools rather than multiple instances of LLMs. That is, ChatGPT, and LLMs in general, should become a remarkable tool for AI systems, and will enable an “LLM in the loop” strategy.

Further research should also grapple with ethical issues related to AI use and consider potential regulatory measures to address these challenges. A key research focus could be the development of methods to enhance AI explainability and to enable the users to provide immediate feedback and reports. The intersection of AI with art and creativity offers intriguing research paths, particularly in legal and ethical domains. Issues like copyright challenges arising from collaborations between AI and humans, and the balance between human input and AI outputs in creative processes, should be thoroughly examined.

Indeed, as a concluding point, the establishment of appropriate regulations, clear procedures, and effective working rules for chatbot systems could significantly enhance output quality, address potential limitations and challenges, and foster safer, more efficient, and more effective use across various domains.

Finally, within the realm of social sciences, the growing number of competing LLMs presents an opportunity to undertake empirical studies. These studies can examine the extent to which different LLMs are utilized, the factors influencing their success or failure, and the degree of user satisfaction, depending on the specific use. Furthermore, empirical studies can explore the motivations behind users’ decisions to either use or avoid using these LLMs.

This work was supported, in part, by the Ministry of Science and Technology, Israel.

This section explains the specific contributions made by each author to the manuscript. The following is a list of the typical roles and responsibilities.

Amos Azaria ( [email protected] ): Conceptualization, Methodology and Writing of Section 1: Introduction; Section 3.1 Incorrect Responses; Section 5: Conclusions & Future Directions; Review & Editing.

Shulamit Reches ( [email protected] ): Conceptualization, Methodology and Writing of Section 2.1: Research and Academic Usage; Section 2.2: Education; Section 2.3: Healthcare; Section 2.5 Mathematics Tasks.

Rina Azoulay ( [email protected] ): Conceptualization, Methodology and Writing of Section 2.4: Programming Assistance; Section 3.2: Ethical Concerns; Section 3: Flowcharts for Efficient ChatGPT Usage.

https://www.business2community.com/statistics/chatgpt

https://tilburgsciencehub.com/tutorials/more-tutorials/chatgpt-article/chat-gpt-research/

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Evidence Review of the Adverse Effects of COVID-19 Vaccination and Intramuscular Vaccine Administration

Vaccines are a public health success story, as they have prevented or lessened the effects of many infectious diseases. To address concerns around potential vaccine injuries, the Health Resources and Services Administration (HRSA) administers the Vaccine Injury Compensation Program (VICP) and the Countermeasures Injury Compensation Program (CICP), which provide compensation to those who assert that they were injured by routine vaccines or medical countermeasures, respectively. The National Academies of Sciences, Engineering, and Medicine have contributed to the scientific basis for VICP compensation decisions for decades.

HRSA asked the National Academies to convene an expert committee to review the epidemiological, clinical, and biological evidence about the relationship between COVID-19 vaccines and specific adverse events, as well as intramuscular administration of vaccines and shoulder injuries. This report outlines the committee findings and conclusions.

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Digital Resource: Evidence Review of the Adverse Effects of COVID-19 Vaccination
Digital Resource: Evidence Review of Shoulder Injuries from Intramuscular Administration of Vaccines
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RNA's hidden potential: New study unveils its role in early life and future bioengineering

Study sheds light on the molecular evolution of rna and its potential applications in nanobiotechnology..

The beginning of life on Earth and its evolution over billions of years continue to intrigue researchers worldwide. The central dogma or the directional flow of genetic information from a deoxyribose nucleic acid (DNA) template to a ribose nucleic acid (RNA) transcript, and finally into a functional protein, is fundamental to cellular structure and functions. DNA functions as the blueprint of the cell and carries genetic information required for the synthesis of functional proteins. Conversely, proteins are required for the synthesis of DNA. Therefore, whether DNA emerged first or protein, continues to remain a matter of debate.

This molecular version of the "chicken and egg" question led to the proposition of an "RNA World." RNAs in the form of 'ribozymes' or RNA enzymes carry genetic information similar to DNA and also possess catalytic functions like proteins. The discovery of ribozymes further fueled the RNA World hypothesis where RNA served dual functions of "genetic information storage" and "catalysis," facilitating primitive life activities solely by RNA. While modern ribosomes are a complex of RNAs and proteins, ribozymes during early evolutionary stages may have been pieced together through the assembly of individual functional RNA units.

To test this hypothesis, Professor Koji Tamura, along with his team of researchers at the Department of Biological Science and Technology, Tokyo University of Science, conducted a series of experiments to decode the assembly of functional ribozymes. For this, they designed an artificial ribozyme, R3C ligase, to investigate how individual RNA units come together to form a functional structure. Giving further insight into their work published on 17 April 2024, in Life , Prof. Tamura states, "The R3C ligase is a ribozyme that catalyzes the formation of a 3',5'-phosphodiester linkage between two RNA molecules. We modified the structure by adding specific domains that can interact with various effectors."

Within ribosomes, which are the site of protein synthesis, RNA units assemble to function as Peptidyl Transferase Center (PTC) in a way such that they form a scaffold for the recruitment of amino acids (individual components of a peptide/protein) attached to tRNAs. This is an important insight into the evolutionary history of protein synthesis systems, but it is not sufficient to trace the evolutionary pathway based on the RNA World hypothesis.

To explore if the elongation of RNA, achieved by linking individual RNA units together, is regulated allosterically, the researchers altered the structure of the R3C ligase. They did this by incorporating short RNA sequences that bind adenosine triphosphate (ATP), a vital energy carrier molecule in cells, into the ribozyme. The team noted that R3C ligase activity was dependent on the concentration of ATP, with higher activity observed at higher concentrations of ATP. Further, an increase in the melting temperature (T m value) indicated that the binding of ATP to R3C ligase stabilized the structure, which likely influenced its ligase activity.

Similarly, on fusing an L-histidine-binding RNA sequence to the ribozyme, they noted an increase in ligase activity at increasing concentrations of histidine (a key amino acid). Notably, the increase in activity was specific to increasing concentrations of ATP or histidine; no changes were observed in response to other nucleotide triphosphates or amino acids. These findings suggest that ATP and histidine act as effector molecules that trigger structural conformational changes in the ribozyme, which further influence enzyme stability and activity.

ATP is the central energy carrier of the cell which supports numerous molecular processes, while, histidine is the most common amino acid found in the active site of enzymes, and maintains their acid-base chemistry. Given, the important roles of ATP and histidine in RNA interactions and molecular functions, these results provide novel insights into the role of RNA in early evolution, including the origin of the genetic code. Furthermore, engineered ribozymes such as the one developed in this study hold significant promise in a myriad of applications including targeted drug delivery, therapeutics, nano-biosensors, enzyme engineering, and synthesis of novel enzymes with uses in various industrial processes.

Overall, this study can offer insights into how the transition from the RNA World to the modern "DNA/Protein World" occurred. A fundamental understanding of the RNA World in turn, can enhance their use in real-life applications.

"This study will lead to the elucidation of the process of 'allostericity-based acquisition of function and cooperativity' in RNA evolution. The RNA-RNA interactions, RNA-amino acid interactions, and allostericity applied in this research can guide the fabrication of arbitrary RNA nanostructures, with various applications," concludes Prof. Tamura.

Biochemistry Research
Cell Biology
Earth Science
Origin of Life
Charles Darwin
Molecular biology
Genetic code
Origin of life

Story Source:

Materials provided by Tokyo University of Science . Note: Content may be edited for style and length.

Journal Reference :

Yuna Akatsu, Hiromi Mutsuro-Aoki, Koji Tamura. Development of Allosteric Ribozymes for ATP and l-Histidine Based on the R3C Ligase Ribozyme . Life , 2024; 14 (4): 520 DOI: 10.3390/life14040520

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