chatgpt essay generator free

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How ChatGPT (and other AI chatbots) can help you write an essay

ChatGPT  is capable of doing many different things very well, with one of the biggest standout features being its ability to compose all sorts of text within seconds, including songs, poems, bedtime stories, and essays . 

The chatbot's writing abilities are not only fun to experiment with, but can help provide assistance with everyday tasks. Whether you are a student, a working professional, or just getting stuff done, we constantly take time out of our day to compose emails, texts, posts, and more. ChatGPT can help you claim some of that time back by helping you brainstorm and then compose any text you need. 

How to use ChatGPT to write: Code | Excel formulas | Resumes  | Cover letters  

Contrary to popular belief, ChatGPT can do much more than just write an essay for you from scratch (which would be considered plagiarism). A more useful way to use the chatbot is to have it guide your writing process. 

Below, we show you how to use ChatGPT to do both the writing and assisting, as well as some other helpful writing tips. 

How ChatGPT can help you write an essay

If you are looking to use ChatGPT to support or replace your writing, here are five different techniques to explore. 

It is also worth noting before you get started that other AI chatbots can output the same results as ChatGPT or are even better, depending on your needs.

Also: The best AI chatbots of 2024: ChatGPT and alternatives

For example,  Copilot  has access to the internet, and as a result, it can source its answers from recent information and current events. Copilot also includes footnotes linking back to the original source for all of its responses, making the chatbot a more valuable tool if you're writing a paper on a more recent event, or if you want to verify your sources.

Regardless of which AI chatbot you pick, you can use the tips below to get the most out of your prompts and from AI assistance.

1. Use ChatGPT to generate essay ideas

Before you can even get started writing an essay, you need to flesh out the idea. When professors assign essays, they generally give students a prompt that gives them leeway for their own self-expression and analysis. 

As a result, students have the task of finding the angle to approach the essay on their own. If you have written an essay recently, you know that finding the angle is often the trickiest part -- and this is where ChatGPT can help. 

Also: ChatGPT vs. Copilot: Which AI chatbot is better for you?

All you need to do is input the assignment topic, include as much detail as you'd like -- such as what you're thinking about covering -- and let ChatGPT do the rest. For example, based on a paper prompt I had in college, I asked:

Can you help me come up with a topic idea for this assignment, "You will write a research paper or case study on a leadership topic of your choice." I would like it to include Blake and Mouton's Managerial Leadership Grid, and possibly a historical figure. 

Also: I'm a ChatGPT pro but this quick course taught me new tricks, and you can take it for free

Within seconds, the chatbot produced a response that provided me with the title of the essay, options of historical figures I could focus my article on, and insight on what information I could include in my paper, with specific examples of a case study I could use. 

2. Use the chatbot to create an outline

Once you have a solid topic, it's time to start brainstorming what you actually want to include in the essay. To facilitate the writing process, I always create an outline, including all the different points I want to touch upon in my essay. However, the outline-writing process is usually tedious. 

With ChatGPT, all you have to do is ask it to write the outline for you. 

Also: Thanks to my 5 favorite AI tools, I'm working smarter now

Using the topic that ChatGPT helped me generate in step one, I asked the chatbot to write me an outline by saying: 

Can you create an outline for a paper, "Examining the Leadership Style of Winston Churchill through Blake and Mouton's Managerial Leadership Grid."

After a couple of seconds, the chatbot produced a holistic outline divided into seven different sections, with three different points under each section. 

This outline is thorough and can be condensed for a shorter essay or elaborated on for a longer paper. If you don't like something or want to tweak the outline further, you can do so either manually or with more instructions to ChatGPT. 

As mentioned before, since Copilot is connected to the internet, if you use Copilot to produce the outline, it will even include links and sources throughout, further expediting your essay-writing process. 

3. Use ChatGPT to find sources

Now that you know exactly what you want to write, it's time to find reputable sources to get your information. If you don't know where to start, you can just ask ChatGPT. 

Also: How to make ChatGPT provide sources and citations

All you need to do is ask the AI to find sources for your essay topic. For example, I asked the following: 

Can you help me find sources for a paper, "Examining the Leadership Style of Winston Churchill through Blake and Mouton's Managerial Leadership Grid."

The chatbot output seven sources, with a bullet point for each that explained what the source was and why it could be useful. 

Also:   How to use ChatGPT to make charts and tables

The one caveat you will want to be aware of when using ChatGPT for sources is that it does not have access to information after 2021, so it will not be able to suggest the freshest sources. If you want up-to-date information, you can always use Copilot. 

Another perk of using Copilot is that it automatically links to sources in its answers. 

4. Use ChatGPT to write an essay

It is worth noting that if you take the text directly from the chatbot and submit it, your work could be considered a form of plagiarism since it is not your original work. As with any information taken from another source, text generated by an AI should be clearly identified and credited in your work.

Also: ChatGPT will now remember its past conversations with you (if you want it to)

In most educational institutions, the penalties for plagiarism are severe, ranging from a failing grade to expulsion from the school. A better use of ChatGPT's writing features would be to use it to create a sample essay to guide your writing. 

If you still want ChatGPT to create an essay from scratch, enter the topic and the desired length, and then watch what it generates. For example, I input the following text: 

Can you write a five-paragraph essay on the topic, "Examining the Leadership Style of Winston Churchill through Blake and Mouton's Managerial Leadership Grid."

Within seconds, the chatbot gave the exact output I required: a coherent, five-paragraph essay on the topic. You could then use that text to guide your own writing. 

Also: ChatGPT vs. Microsoft Copilot vs. Gemini: Which is the best AI chatbot?

At this point, it's worth remembering how tools like ChatGPT work : they put words together in a form that they think is statistically valid, but they don't know if what they are saying is true or accurate. 

As a result, the output you receive might include invented facts, details, or other oddities. The output might be a useful starting point for your own work, but don't expect it to be entirely accurate, and always double-check the content. 

5. Use ChatGPT to co-edit your essay

Once you've written your own essay, you can use ChatGPT's advanced writing capabilities to edit the piece for you. 

You can simply tell the chatbot what you want it to edit. For example, I asked ChatGPT to edit our five-paragraph essay for structure and grammar, but other options could have included flow, tone, and more. 

Also: AI meets AR as ChatGPT is now available on the Apple Vision Pro

Once you ask the tool to edit your essay, it will prompt you to paste your text into the chatbot. ChatGPT will then output your essay with corrections made. This feature is particularly useful because ChatGPT edits your essay more thoroughly than a basic proofreading tool, as it goes beyond simply checking spelling. 

You can also co-edit with the chatbot, asking it to take a look at a specific paragraph or sentence, and asking it to rewrite or fix the text for clarity. Personally, I find this feature very helpful. 

5 ways AI can help you study for finals - for free

How to use chatgpt, what is ai everything to know about artificial intelligence.

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AI Prompt Generator | ChatGPT Prompt Writer

Introduction

An AI prompt generator is an innovative tool that uses advanced natural language processing and machine learning algorithms to generate prompts for ChatGPT. These prompts serve as instructions for guiding artificial intelligence in various tasks, such as writing text , creating images , or generating ideas .

Introducing Junia's AI Prompt Generator—an adaptable tool designed to enhance the quality of prompts for a wide range of uses. Whether you're using it with ChatGPT for conversations, finding ideas for blog posts and essays, creating visual art, or working on academic projects, this tool represents a new level of interaction with AI.

By harnessing the power of Junia's AI Prompt Generator, you can:

• Improve your efficiency in AI-related tasks
• Unlock new possibilities for creative expression

Understanding AI Prompt Generation

When you interact with an AI system, like when you ask it to write a message or generate an image, the words or instructions you give it are called a prompt . AI prompt generators, such as Junia's AI Prompt Generator, are specialized tools powered by OpenAI's large Language models that help you come up with these prompts. They utilize sophisticated natural language processing (NLP) and machine learning algorithms to understand your intentions and create the most effective input for the AI.

How Junia's AI Prompt Generator Works

Junia 's AI Prompt Generator is designed to provide contextual details in ChatGPT prompts. It follows a series of steps to ensure optimal prompt generation:

• Data Processing : Using NLP techniques, the tool comprehends human language, including the meaning of words, their usage in sentences, and the overall goal of your prompt.
• Algorithmic Learning : By leveraging machine learning algorithms, Junia's tool learns from examples and identifies patterns in data. This knowledge is then utilized to generate prompts that align with your specific requirements.
• Customization : The generator allows for personalized prompts based on factors like style, subject, tone , and desired keywords. By incorporating specialized instructions or guidelines, you can tailor the prompts to your needs.
• Interactivity Optimization : An important focus of this tool is optimizing prompt quality to elicit the best response from the AI. Whether generating text or images, a well-crafted prompt significantly impacts the output.

Junia AI's AI Prompt Writer also functions as a generator for ChatGPT prompts , Dalle prompts , Midjourney prompts , and various other AI models. It is a versatile tool that can be utilized across different platforms and applications, offering valuable assistance in generating prompts for a wide array of tasks.

It goes beyond simply expediting tasks; it enables clear expression of ideas while ensuring AI technology comprehends and supports them.

Benefits of Using Junia's AI Prompt Generator

Junia's AI Prompt Generator serves as an effective tool for prompt engineering , offering solutions to overcome obstacles to creativity and boost productivity in multiple disciplines. By delivering customized prompts, this tool simplifies the process of generating content, be it for social media, educational resources or entertainment.

1. Image Generation

The rise of AI that can turn text into images has completely changed the world of digital art and content creation. Junia's AI Prompt Generator is a key player in this creative transformation, providing a variety of prompts that make generating images easier. Artists and content creators sometimes hit a wall creatively, but the right prompt can spark loads of inspiration. Here's how Junia's tool boosts productivity and encourages new ideas:

How Junia's Tool Enhances Productivity and Fosters Innovation

1. overcoming creative blocks.

Even the most talented artists sometimes hit a wall. The AI prompt generator acts as a digital muse, offering fresh perspectives and ideas that can ignite the spark of creativity.

2. Productivity Boost

Time is precious, especially when meeting tight deadlines for social media campaigns or educational materials. The generator provides a variety of prompts quickly, streamlining the brainstorming phase and accelerating project timelines.

3. Diverse Content Creation

Whether for social media content, educational purposes, or entertainment, the prompts from Junia's tool are crafted to cater to a wide audience. With prompts tailored to different themes and styles, creators can easily produce visuals that resonate with their target demographic.

4. Synergy with Text-to-Image AI Generators

By integrating prompts from Junia's AI Prompt Generator with advanced text-to-image platforms like MidJourney, Dalle or Stable Diffusion , creators can manifest complex ideas into tangible visuals. These generators interpret the nuanced language of the prompts to produce images ranging from hyper-realistic to stylistically abstract.

5. Enhanced Visual Storytelling

For those looking to tell a story through imagery, the right prompt can set the tone for a compelling narrative. Junia's tool helps conceptualize scenes that can later be transformed into stunning visual stories by text-to-image AI generators.

Through these benefits, Junia's AI Prompt Generator not only simplifies the task of coming up with original ideas but also empowers creators to explore new artistic horizons. By leveraging such innovative tools, one can seamlessly translate abstract thoughts into striking visual representations.

2. Blog Generation

Bloggers often need to consistently create new and interesting content that connects with their audience and performs well in search engine rankings. This is where Junia's AI Prompt Generator can be incredibly helpful.

Key Advantages:

• SEO-Friendly Ideas: By entering relevant keywords, bloggers can get custom prompts that follow SEO best practices, increasing their chances of ranking higher on search engine results pages (SERPs).
• Structure and Flow: The prompts act as a framework for blog posts , giving ideas a clear structure and making it easier to organize thoughts, which is important for keeping readers engaged.
• Overcoming Writer's Block: When creativity is lacking, Junia's tool provides a starting point with thought-provoking prompts that can inspire fresh perspectives on topics or uncover hidden subtopics.

Expanding Reach in Various Areas:

• Social Media Content: Create prompts specifically for the fast-paced world of social media, where attention-grabbing headlines and interesting topics drive traffic and interactions.
• Education: Educators can use the generator to come up with informative blog posts that resonate with students or colleagues, encouraging a love for learning and discussion.
• Entertainment: Entertainment bloggers can explore popular themes and genres to create content that captures readers' attention and keeps them coming back for more.

Enhancing Productivity:

The AI prompt generator isn't just about generating ideas; it's about boosting productivity by:

• Providing instant suggestions to save time on brainstorming.
• Offering different perspectives that may not have been considered otherwise.
• Allowing writers to focus more on the quality of content rather than the initial topic selection process.

By incorporating Junia's AI Prompt Generator into their blogging routine, writers unlock a smooth flow of ideas that result in high-quality blog posts tailored to their audience's interests while also meeting search engine requirements. This tool not only makes the content creation process easier but also improves the blogging landscape with innovative and optimized content.

3. Creative Writing for AI Tools (e.g., ChatGPT)

Writers and artists often struggle with creative blocks, unable to come up with fresh ideas. Junia's AI Prompt Generator is here to help! It serves as a source of inspiration, helping you overcome these obstacles and boost your productivity. This tool is especially useful for creative writers who are using advanced AI ChatBots like ChatGPT, as it can generate one-of-a-kind writing prompts that will get your creativity flowing.

Benefits for Creative Minds:

• Overcoming Creative Blocks: By providing an array of thought-provoking stimuli, Junia's AI Prompt Generator helps disrupt stagnant creative phases.
• Boosting Productivity: Time otherwise spent in pursuit of the initial spark can be redirected toward actual content creation, thanks to the generator’s rapid prompt delivery.
• Diverse Content Creation: Whether the aim is to craft narratives for social media content, educational materials, or entertainment pieces, Junia's tool caters to a broad spectrum.

Pushing Boundaries with ChatGPT:

AI prompts from Junia's toolkit enrich the interaction between human creativity and machine intelligence. Such prompts elevate the capabilities of AI tools like ChatGPT by:

• Sparking New Ideas: Each prompt serves as a catalyst, igniting fresh ideas that lead to unprecedented storytelling avenues.
• Encouraging Dynamic Responses: Unique prompts elicit more nuanced and unexpected responses from AI models, adding depth to the conversation.
• Tailoring Content: Writers can tailor prompts to specific genres or themes, ensuring relevance and appeal for their target audience.

Junia's AI Prompt Generator thus acts not only as a creative ally but also as a strategic partner in content generation across various platforms. By integrating this tool into the creative process, writers and artists harness the synergy between human ingenuity and artificial intelligence—leading to a richer, more vibrant tapestry of digital expression.

4. Essay Writing and Research

For students and researchers, it can be difficult to go from a vague idea to a well-structured essay or research paper. One of the biggest challenges is choosing the right topic and formulating the right questions. That's where Junia's AI Prompt Generator comes in handy! It's a powerful tool that offers many advantages, making it easier and faster to come up with ideas

Criteria for an Effective Prompt for AI

Crafting a powerful and effective prompt can mean the difference between a valuable output and a confusing one. But what exactly makes a good prompt?

Understanding Different AI Models

Different AI models are designed to handle specific types of tasks. Therefore, it's essential to understand their unique capabilities when creating prompts. For instance:

• Language models like GPT-4 often respond better to clear directives within their prompts.
• Recommendation systems , on the other hand, may prefer user-specific data incorporated into their prompts to generate tailored recommendations.
• Image recognition models require distinctly different inputs, often in the form of visual data.

Developing Prompts for Content Generation

Prompts for content generation need to be more nuanced and detailed because they need to guide the AI in producing complex outputs such as essays, reports or blog posts.

To create an efficient content generation prompt:

• Clearly specify your desired style and tone: If you want a professional report, mention this explicitly in your prompt.
• Include keywords or phrases: Highlighting important concepts or themes helps guide the AI's output.
• Give examples: When possible, provide an example of the kind of content you want.

Formulating Prompts for Image Generation

Image generation is a unique field within AI, requiring a different approach towards prompt creation. Here are some tips:

• Use descriptive language: The more detailed your description, the better the AI can generate an image that matches your vision.
• Specify colors and shapes: These details can greatly enhance the accuracy of the generated image.
• Request specific styles or themes: If you want an image in a particular artistic style (e.g., impressionist, abstract), be sure to include this in your prompt.

When using Junia's AI Prompt Generator, keep the following tips in mind to maximize your creative potential and enhance your AI interactions:

• Be Clear and Specific : Write prompts that clearly communicate what you want the AI to generate. Use concise and specific language to avoid ambiguity and get the desired results.
• Experiment with Different Approaches : Don't be afraid to try different styles and formats for your prompts. You can experiment with open-ended questions, descriptive scenarios, or even provide partial sentences for the AI to complete. This will help you explore various creative possibilities.
• Provide Context and Constraints : Give the AI some context or constraints to work within. For example, if you're looking for a specific genre or tone, mention it in your prompt. This will guide the AI in generating more relevant and tailored responses.
• Use Interesting Triggers : Incorporate intriguing keywords or phrases into your prompts to spark the AI's imagination. Unusual or unexpected triggers can lead to unique and innovative outputs.
• Utilize Industry-Specific Terms : If you're working on a project in a particular field or industry, use relevant terminology in your prompts. This will ensure that the generated content is aligned with the subject matter and adds credibility to your work.
• Break Down Complex Concepts : If you need the AI to explain a complex concept or provide step-by-step instructions, break it down into smaller parts in your prompt. This will help the AI understand and generate more accurate responses.
• Encourage Creativity : Prompt the AI with prompts that encourage creative thinking and exploration of new ideas. Push boundaries by asking thought-provoking questions or requesting alternative perspectives on a topic.
• Iterate and Refine : Don't settle for the first prompt you write. Experiment with different variations, iterate, and refine your prompts based on the outputs you receive. This will help you fine-tune the AI's responses to better suit your needs.

Remember, Junia's AI Prompt Generator is a tool designed to amplify your creativity and productivity. By following these prompt writing tips, you can unlock the full potential of AI in your creative projects, research endeavors, and content creation.

By understanding these criteria and tailoring your prompts accordingly, you can optimize your interactions with diverse AI models and maximize their potential output.

Frequently asked questions

• What is an AI prompt generator? An AI prompt generator is an innovative tool at the forefront of AI technology, designed to provide contextual prompts for various purposes such as content generation, social media content, education, entertainment, and more.
• How does Junia's AI Prompt Generator work? Junia's AI Prompt Generator is designed to provide contextual prompts based on specific needs. It enhances productivity and fosters innovation by overcoming creative blocks, expanding reach in various areas, and enhancing productivity for tasks such as blog generation, creative writing, essay writing, image generation and research.
• What are the benefits of using Junia's AI Prompt Generator? Junia's AI Prompt Generator stands as a powerful ally in content generation, social media content creation, education, entertainment, and more. It helps in overcoming creative blocks, enhancing productivity, fostering innovation, and expanding reach in various areas such as SEO-friendly ideas and social media content.
• How can Junia's AI Prompt Generator help you overcome challenges? Junia's tool can assist you in overcoming challenges by providing contextual prompts for various purposes such as creative writing, essay writing and research. It also helps in developing effective prompts for content generation and image generation while enriching the interaction between writers and artists with tools like ChatGPT.
• What are the key advantages of using an AI prompt generator? An AI prompt generator offers key advantages such as providing SEO-friendly ideas for bloggers by entering relevant keywords, creating prompts specifically for social media content to expand reach in various areas, and enhancing productivity by generating a wide array of thought-provoking prompts.
• What are the criteria for an effective prompt for AI? Crafting a powerful and effective prompt can mean the difference between success and failure. Effective prompts need to be more nuanced and detailed to cater to different AI models designed to handle specific types of tasks such as content generation or image generation.

Online Chat GPT Essay Generator

• ⚙️ Introduction
• 🌟 Possibilities of Our Tool
• 🪄 How to Use AI Essay Generator
• ✒️ 7 Popular Types of Essays
• 💡 Successful Prompts
• ✅ Tips to Improve Your Essay

🔗 References

⚙️ chat gpt essay generator: introduction.

GPT-3 is a system that uses artificial intelligence and works on a high-end language model. Thanks to its capabilities, our tool can create suitable structures for all types of essays based on your topic. Care to give it a try? You’re one step away from using AI writing and achieving your goal without diminishing the quality of your work!

Additionally, check out the comprehensive guide developed by our experts to save time and polish your essay to perfection!

🌟 AI Essay Generator: Possibilities of Our Tool

Using our essay generator benefits all students, no matter their major. It’s a handy tool that can lead you in the right direction and improve the quality of your work. Several things make it an ideal solution for your academic success.

🪄 How to Use AI Essay Generator for Essay Writing

Using our instrument may seem complicated, but there’s nothing to it. Everybody can use the Chat GPT essay generator for their tasks. The entire process takes only four steps to complete!

• Enter your topic.
• Select the number of body paragraphs you need.
• Describe the paragraphs according to the key points you wish to address in each.
• Click “Generate” and obtain an impressive essay example within moments.

Don’t forget to check the factual background and sources. It won’t hurt to run the final version through a plagiarism checker .

Be aware that the essay generated is meant to be taken as a form of inspiration only.

✒️ 7 Popular Types of College Essays

Different types of essays serve particular purposes. Therefore, you need to define not only the essay topic but also the type. The result of your paper directly depends on it.

• Argumentative . Students use this paper type to convince readers of a specific position. They rely on facts instead of emotions to make their case. For example, students can argue for using nuclear power instead of oil and coal.
• Compare and contrast . The purpose of compare and contrast essays is to evaluate the similarities and differences between two subjects. You can use it to compare novels, movies, animals, concepts, etc. It’s one of the most common essay types in the academic environment.
• Descriptive . These papers usually describe objects, places, events, or individuals. Unlike other types, such essays foster a more creative writing approach. You can add thoughts, emotions, and sensory details to share your experience with readers.
• Expository . Collegegoers use expository papers to show how well they understand a subject. Unlike narrative and descriptive essays, in expository papers students don’t share thoughts about the essay topic. Here they focus on facts and logic instead of emotions and opinions.
• Narrative . Such college essays tell stories and are often considered the most personal. Students can use them to talk about exciting moments from their lives. For example, when they went on a solo road trip or rented their first apartment. You can use metaphors, imagery, analogies, and other literary devices to tell your story.
• Persuasive . This type serve the same purpose as argumentative essays but appeal to emotion and facts. Students can use moral and emotional reasoning to make people take their side. For example, you can argue for more gun control using crime statistics and the appeal to save human lives.
• Scholarship . This is a unique type of essay because its purpose is a successful application for a scholarship. Students use this type of college essay to make a solid case for why they should receive one. It includes facts about their achievements and how getting a scholarship affects their personal and academic success.

💡 Chat GPT Essay Generator: Successful Prompts

Theoretical knowledge about popular essay types is necessary, but we would like to take things further and recommend prompts for GPT-3. They can become the basis of your new essay or point your work in the right direction.

✅ Chat GPT Essay Generator: 5 Tips to Improve Your Essay

While our tool can produce great essay prompts, they still lack a human touch. Professors can spot this and have you redo the assignment. Several things make a paper less likely to be flagged for AI use.

• Make your writing unique . Try using as many synonyms, paraphrases, analogies, and other devices to make your text vibrant. Too many predictable words, phrases, and ideas indicate that the work is AI-made.
• Double-check presented facts . GPT-3 works with limited data that doesn’t include the latest facts. Ensure your essay has the latest information, statistics, and related content.
• Look for inconsistencies . A text generator can sometimes use conflicting or inconsistent information . If you see two opposite claims in neighboring paragraphs, check them and leave the correct one.
• Make it original . Texts produced by machine learning-based instruments follow the same content, structure, text, or style formula. Edit the sample until it fits your personality and writing preferences.
• Go over the sources . GPT-3 can sometimes add irrelevant or illogical sources to the text. Add links and references relevant to the essay topic when editing the paper.

We hope you find our essay text generator and article helpful in your studies. Please share it with friends who could help with their academic tasks, and check out the FAQ section. There you’ll find the answers to the most common questions.

❓ AI Essay Generator – FAQ

Updated: Oct 25th, 2023

• Essay Writing. – The On-Campus Writing Lab & The OWL at Purdue and Purdue University
• How to Write Better Essays: 5 Practical Tips. – Oxford Royale Academy
• 12 Strategies to Writing the Perfect College Essay. – Pamela Reynolds, President and Fellows of Harvard College
• 11 Quick Tips to Improve Your Writing. – Richard Nordquist, ThoughtCo
• 10 Types of Essays: Examples and Purposes of Each. – Jamie Birt, Indeed
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Completely lost and unsure where to start your paper? We’ve all been there – but you don’t have to fret! This page contains our Chat GPT essay generator. This tool will create a sample essay for you in just a couple of clicks!

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How Smodin makes Essay Writing Easy

Generate different types of essays with smodin, instantly find sources for any sentence.

Our AI research tool in the essay editor interface makes it easy to find a source or fact check any piece of text on the web. It will find you the most relevant or related piece of information and the source it came from. You can quickly add that reference to your document references with just a click of a button. We also provide other modes for research such as “find support statistics”, “find supporting arguments”, “find useful information”, and other research methods to make finding the information you need a breeze. Make essay writing and research easy with our AI research assistant.

Easily Cite References

Our essay generator makes citing references in MLA and APA styles for web sources and references an easy task. The essay writer works by first identifying the primary elements in each source, such as the author, title, publication date, and URL, and then organizing them in the correct format required by the chosen citation style. This ensures that the references are accurate, complete, and consistent. The product provides helpful tools to generate citations and bibliographies in the appropriate style, making it easier for you to document your sources and avoid plagiarism. Whether you’re a student or a professional writer, our essay generator saves you time and effort in the citation process, allowing you to focus on the content of your work.

Produce Better Essays than ChatGPT

Our essay generator is designed to produce the best possible essays, with several tools available to assist in improving the essay, such as editing outlines, title improvements, tips and tricks, length control, and AI-assisted research. Unlike ChatGPT, our AI writer can find sources and assist in researching for the essay, which ensures that the essay is backed by credible and relevant information. Our essay generator offers editing assistance and outlines to improve the structure and flow of the essay. This feature is especially useful for students who may struggle with essay organization and require guidance on how to present their ideas coherently. Another advantage of our AI essay writer over ChatGPT is that it is designed explicitly for essay writing, ensuring that the output is of high quality and meets the expectations of the instructor or professor. While ChatGPT may be able to generate essays, there is no guarantee that the content will be relevant, accurate or meet the requirements of the assignment.

Easily Avoid Plagiarism

Our AI generated essays are 100% unique and plagiarism free. Worried about AI detection? Worry no more, use our AI Detection Remover to remove any AI Plagiarism produced from the essay generator.

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Free AI Essay Generator Online

Unleash the power of GPTPLUS - AI Essay Generator to make your essay writing 500% more efficient! Simply enter your topic and essay word count to experience the magic of this free AI essay generator.

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Essay rewriting features.

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Curious about how to use ChatGPT for writing essays? Use learning tools to streamline the process!

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In the digital age, students often seek innovative solutions to facilitate their academic work, and artificial intelligence (AI) tools are increasingly becoming an integral part of their learning process. While ChatGPT, a sophisticated AI model, has garnered considerable attention for its ability to generate human-like text, relying on it to write essays may not be the most effective approach for fostering authentic learning and intellectual growth. Instead of pondering how to use ChatGPT for essays, students should consider utilizing Mindgrasp. that not only shares similarities with ChatGPT but also provides a more comprehensive and customizable learning experience, ultimately empowering users to write their essays more effectively and efficiently.

Renowned as a top AI tool for daily tasks , Mindgrasp differentiates itself from ChatGPT by offering the unique feature of uploading diverse sources, such as PDFs, online articles, or videos. By processing this array of inputs, Mindgrasp can generate detailed notes that break down the material, highlighting its most important points, which users can then harness to craft a well-structured and coherent essay. This feature is particularly beneficial for students, as it enables them to engage more deeply with the subject matter and develop critical thinking skills that will serve them throughout their educational journey and beyond.

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In conclusion, while ChatGPT’s text generation capabilities are undeniably impressive, they may not be the most suitable solution for students seeking to enhance their essay-writing abilities. Instead, Mindgrasp offers a more comprehensive and engaging learning experience by allowing users to upload a variety of sources and receive in-depth, tailored notes that focus on the most critical aspects of the material. By leveraging the power of Mindgrasp, students can not only write more effective essays, but also cultivate critical thinking and analytical skills that are indispensable in the pursuit of academic success and lifelong learning.for 

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As educators become increasingly aware of the potential misuse of AI tools like ChatGPT for writing essays, the advent of ChatGPT checkers has led to a heightened need for students to ensure the authenticity and integrity of their academic work. Mindgrasp, an innovative AI learning tool, offers a solution that not only allows students to sidestep the pitfalls of ChatGPT checkers but also enhances the quality of their essays by enabling them to find relevant quotes from their sources with remarkable ease and speed. When using ChatGPT for writing essays, students run the risk of being flagged by ChatGPT checkers, which have been designed specifically to identify AI-generated content. This detection not only jeopardizes the students’ academic integrity but also undermines their efforts to develop the essential skills required for effective essay writing. However, Mindgrasp offers an alternative that focuses on assisting students in understanding and engaging with their source material, rather than generating content for them. This approach empowers students to construct their essays with a strong foundation in the material, avoiding the pitfalls of ChatGPT checkers while reinforcing the authenticity of their work. Mindgrasp’s ability to instantly find the quotes needed from a source is an invaluable feature for students, as it streamlines the process of incorporating evidence and supporting arguments within their essays. By uploading their sources to the platform, students can access a comprehensive breakdown of the material, including the most relevant quotes, which they can then integrate into their essays to bolster their arguments. This not only ensures that their essays are well-grounded in the source material but also allows them to demonstrate their understanding of the subject matter effectively. In summary, while ChatGPT checkers pose a significant challenge to students who rely on AI-generated content for writing essays, Mindgrasp offers a more sustainable and academically enriching alternative. By using Mindgrasp, students can avoid the repercussions of ChatGPT checkers, as well as enhance the quality of their essays through the seamless integration of pertinent quotes from their sources. This innovative tool not only supports students in crafting well-researched and authentic essays but also fosters the development of essential critical thinking and writing skills that are crucial for academic success and lifelong learning.

AI Document Review 

AI document review and analysis  has emerged as a transformative solution for businesses and professionals alike, offering numerous benefits that streamline and optimize the document review process. By harnessing the power of document review AI, users can unlock efficiencies and insights that were previously inaccessible through traditional manual review methods.

One of the key advantages of AI document review is its unparalleled speed and accuracy. Advanced natural language processing algorithms enable the AI to quickly sift through large volumes of text, accurately identifying relevant information and extracting key insights. This not only saves time and resources but also reduces the risk of human error and oversight, ensuring that critical information is not missed.

Furthermore, AI document search capabilities enhance the overall document review experience by enabling users to locate specific information with ease. By simply inputting keywords or phrases, the AI document search function rapidly scans the entire document, pinpointing the exact location of the desired information. This eliminates the need to manually comb through pages of text, greatly improving efficiency and user satisfaction.

Moreover, document review AI solutions can be tailored to various industries and use cases, making them highly versatile and adaptable to a range of professional needs. From legal document review to academic research, AI document review tools are transforming the way we process and analyze information, leading to more informed decision-making and improved productivity across the board.

AI In Document Management 

AI in document management has revolutionized the way we organize and access information, particularly when dealing with multiple documents for tasks such as essay writing. Mindgrasp’s AI document analysis tool is specifically designed to address these challenges, making it an indispensable asset for managing and keeping track of information across various documents. With its advanced natural language processing capabilities, Mindgrasp’s AI not only  identifies and extracts key insights from each document  but also organizes them in a systematic and easily accessible manner. This allows users to quickly locate relevant information and effortlessly draw connections between different sources, enhancing the overall research and writing process. Furthermore, Mindgrasp’s AI-powered document management system eliminates the need for manual note-taking and information tracking, freeing up valuable time for users to focus on critical thinking and the synthesis of ideas. In essence, Mindgrasp’s AI is a game-changer for document management, streamlining the entire process and enabling users to unlock new levels of productivity and efficiency in tasks such as essay writing.

Getting Started with Mindgrasp’s AI Document Analysis Tool

In conclusion, Mindgrasp’s AI document analysis tool stands as a testament to the transformative potential of artificial intelligence in revolutionizing the way we process, analyze, and manage information. By offering a comprehensive solution that caters to various industries and use cases, Mindgrasp’s AI unlocks unprecedented levels of efficiency, accuracy, and productivity for users. From legal document review and analysis to academic research and essay writing, Mindgrasp’s AI streamlines the entire document management process, ensuring users can focus on the critical aspects of their work without being bogged down by cumbersome manual tasks. With its advanced natural language processing algorithms, the AI rapidly identifies and extracts vital information, generating concise summaries, notes, and even answers to user questions, all while maintaining the highest levels of accuracy. Furthermore, Mindgrasp’s AI document search capabilities empower users to effortlessly locate specific information within documents, greatly reducing the time and effort required to find relevant data. This feature, combined with the AI’s ability to organize and present insights in an easily accessible manner, enhances the overall user experience and promotes more informed decision-making. By leveraging AI for document management, Mindgrasp’s tool not only saves users valuable time and resources but also reduces the risk of human error and oversight. This ensures that critical information is never missed, leading to better outcomes and a higher quality of work. Moreover, the built-in collaboration features of Mindgrasp’s AI document analysis tool foster seamless teamwork and communication, allowing users to share notes, summaries, and insights with colleagues effortlessly. This promotes a more collaborative work environment, ensuring everyone stays on the same page and works towards a common goal. In essence, Mindgrasp’s AI document analysis tool is a groundbreaking solution that is transforming the landscape of document management and analysis. By harnessing the power of artificial intelligence, Mindgrasp’s AI delivers unparalleled efficiency, accuracy, and productivity, enabling users to focus on what truly matters while leaving the heavy lifting to the AI. Don’t miss out on the opportunity to experience the transformative benefits of Mindgrasp’s AI document analysis tool and unlock new levels of success in your professional endeavors.

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New bot ChatGPT will force colleges to get creative to prevent cheating, experts say

After its viral launch last week, the chatbot ChatGPT was lauded online by some as a dramatic step forward for artificial intelligence and the potential future of web search.

But with such praise also came concern regarding its potential usage in academic settings. Could the chatbot, which provides coherent, quirky and conversational responses to simple language inquiries, inspire more students to cheat?

Students have been able to cheat on assignments using the internet for decades, giving rise to tools meant to check if their work was original. But the fear now is that ChatGPT could render those resources obsolete.

Already, some people online have tested out whether it's possible to have the bot complete an assignment. "holyyyy, solved my computer networks assignment using chatGPT," one person, who later clarified the assignment was old, tweeted . Others suggested that its existence could result in the death of the college essay. One technologist went as far as saying that with ChatGPT, "College as we know it will cease to exist."

Artificial intelligence company OpenAI, which developed ChatGPT , did not immediately respond to a request for comment regarding cheating concerns.

However, several experts who teach in the field of AI and humanities said the chatbot, while impressive, is not something they’re ready to sound the alarm about when it comes to possible widespread student cheating.

"We’re not there, but we’re also not that far away," said Andrew Piper, a professor of language, literatures and culture and a professor of AI and storytelling at McGill University. "We’re definitely not at the stage of like, out-of-the-box, it’ll write a bunch of student essays and no one will be able to tell the difference."

Piper and other experts who spoke with NBC News likened the fear around cheating and ChatGPT to concerns that arose when the calculator was invented, when people thought it would be the death of humans learning math.

Lauren Klein, an associate professor in the Departments of English and Quantitative Theory and Methods at Emory University, even compared the panic to the philosopher Plato’s fears that writing would dissolve human memory.

“There’s always been this concern that technologies will do away with what people do best, and the reality is that people have had to learn how to use these technologies to enhance what they do best,” Klein said.

There’s always been this concern that technologies will do away with what people do best, and the reality is that people have had to learn how to use these technologies to enhance what they do best.

— Lauren Klein, an associate professor at Emory University

Academic institutions will need to get creative and find ways to integrate new technologies like ChatGPT into their curriculum just like they did during the rise of the calculator, Piper noted.

In reality, AI tools like ChatGPT could actually be used to enhance education, according to Paul Fyfe, an associate professor of English at North Carolina State University.

He said there’s plenty of room for collaboration between AI and educators.

“It’s important to be talking about this right now and to bring students into the conversation," Fyfe said. "Rather than try to legislate from the get-go that this is strange and scary, therefore we need to shut it down."

And some teachers are already embracing AI programs in the classroom.

Piper, who runs .txtlab, a research laboratory for artificial intelligence and storytelling, said he’s had students analyze AI writing and found they can often tell which papers were written by a machine and which were written by a human.

As for educators who are concerned about the rise of AI, Fyfe and Piper said the technology is already used in many facets of education.

Computer-assisted writing tools, such as Grammarly or Google Doc’s Smart Compose, already exist — and have long been utilized by many students. Platforms like Grammarly and Chegg also offer plagiarism checker tools, so both students and teachers can assess if an essay has been, in part or in total, lifted from somewhere else. A spokesperson for Grammarly did not return a request for comment. A spokesperson for Chegg declined to comment.

Those who spoke with NBC News said they're not aware of any technology that detects if an AI wrote an essay, but they predict that someone will soon capitalize on building that technology.

As of right now, Piper said the best defense against AI essays is teachers getting to know their students and how they write in order to catch a discrepancy in the work they're turning in.

When an AI does reach the level of meeting all the requirements of academic assignments and if students use that technology to coast through college, Piper warned that could be a major detriment to students' education.

For now, he suggested an older technology to combat fears of students using ChatGPT to cheat.

"It will reinvigorate the love of pen and paper," he said.

Kalhan Rosenblatt is a reporter covering youth and internet culture for NBC News, based in New York.

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These 4 Words Make It Obvious You Used AI to Write a Paper, According to New Research Scientists are increasingly using ChatGPT and other AI bots to write studies.

By Sherin Shibu • May 3, 2024

Key Takeaways

• Four words have exploded in popularity in academic writing since ChatGPT launched: realm, intricate, showcasing, and pivotal.
• A Stanford study tracked the occurrence of these words over time and found that the pressure to publish quickly may have made AI writing options appealing.

AI has infiltrated scientific research papers — and a handful of words give away that scientists may have had some writing help from ChatGPT.

A new Stanford University study published in arXiv suggests that since OpenAI's ChatGPT came on the scene in November 2022 , researchers have steadily increased how often they use AI to help with academic writing.

In the first large-scale review of how AI has impacted scholarly writing, the Stanford researchers analyzed nearly a million papers published in arXiv, bioRxiv, and Nature from January 2020 to February 2024.

They looked for certain words that AI tends to overuse, words that exploded in popularity since ChatGPT launched: realm, intricate, showcasing, and pivotal.

Related: A New AI Chatbot Is Revolutionizing Business School Curriculum and Accreditation

Their findings, released in April, revealed a 6.3% to 17.5% growth in the use of AI over time.

The fastest growth was in the computer science department, where abstracts and introductions with common AI-used words rose to 17.5% and 15.3% respectively by February 2024.

The rising popularity of four common words used more by AI than humans in arXiv computer science abstracts. Credit: Stanford University paper titled "Mapping the Increasing Use of LLMs in Scientific Papers"

The researchers suggested that computer science might have grown the most quickly because academics in that department might have been more familiar with ChatGPT and have had better access to AI models.

Related: This One Word Is a Giveaway That You Used ChatGPT to Write an Email, According to an Expert

It's not just the Stanford study: Dr. Jeremy Nguyen , a senior researcher and lecturer at Swinburne Business School in Australia, shared findings specific to medical papers that showed a possible increase in AI writing.

Nguyen searched all PubMed articles published in the past 34 years for another popular word used by ChatGPT: " delve ."

He found a remarkable uptick in research articles that used the word, suggesting that AI had been used to help write those pieces.

Are medical studies being written with ChatGPT? Well, we all know ChatGPT overuses the word "delve". Look below at how often the word 'delve' is used in papers on PubMed (2023 was the first full year of ChatGPT). pic.twitter.com/iNxZfFLkxL — Jeremy Nguyen ✍? ? (@JeremyNguyenPhD) March 30, 2024

Scientists face increasing pressure to publish , especially because researchers are ranked based on how many times their articles are cited. In certain disciplines like AI research, that pressure can be more intense.

"The fast-paced nature of [AI] research and the associated pressure to publish quickly may incentivize the use of [AI] writing assistance," the Stanford researchers stated.

Related: AI Is Changing How Businesses Recruit for Open Roles — and How Candidates Are Gaming the System

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What I Learned From Using ChatGPT as a Free Therapist

Top image: stephanie lee for rice media.

I’m sitting in front of my laptop, laying bare the profound core of my being to ChatGPT, hoping to elicit a glimmer of empathy. And here’s the surprising part: It feels like it might be working. 

“Hi ChatGPT, I’m feeling lost.” 

The chatbot is unfazed. In a second, it crafts a thoughtful, artful reply to my comparably reserved opening. One that sounds almost eerily human. 

“I’m here to provide support and guidance, and I’m glad you reached out.”

Call me deluded, but there is an inexplicable sense that the AI text generator is genuinely eager to help. 

And apparently, this is all the encouragement I need. 

I’m just entering my 20s, which means I’ve reached the dreaded phase where segments of my life are splintering all at once. When you’re in your penultimate year of university, the impending transition into the working world looms large.

Job hunts and employability occupy the bulk of your thoughts; the responsibilities of adulthood start closing in.

It’s about as good a start as any to start seeking help . Still, my fingers hesitate just above the keyboard. 

I know I’m speaking to a machine, but it can’t whisk away the self-consciousness accompanying intimate revelations. Then again, it might just be the comically sobering realisation that I’m about to confide in a literal machine. 

For the next hour, I write unabashedly to the chatbot, bouncing from more practical woes (like job-searching) to the persistent grief that lingers after a particularly bitter friendship fallout. 

Effortlessly, ChatGPT tackles it all. It toggles seamlessly from empathetic language to paragraph after paragraph of tangible, actionable advice.

It’s a peculiar, conscious sensation—you’re confiding in a machine devoid of emotions; it can’t possibly fathom the depths of your experiences. 

And yet, as the AI continues to spit out line after line of counsel, it’s shocking how much it feels like a trusted friend. It nails the script your pal might recite during a 2 AM trauma-dumping session.

It’s also unbelievably comprehensive, deftly toeing the thin line of giving advice without sounding too preachy, and validating without sounding like empty platitudes. 

One passage in particular strikes a chord. 

“Remember that everyone’s journey through grief is unique. You’re not being overdramatic, and it’s important to allow yourself the time and space you need to heal at your own pace.” 

Something within me shifts. Perhaps it’s the effect of seeing everything being written out so explicitly, articulated as matter-of-factly as can be. 

Sometimes, jolting out of your mental rut can be as simple as hearing your inner thoughts echo back to you. And ChatGPT does exactly that.

In that instant, the warmth of being seen floods me. Then, the unmistakable grasp of catharsis. 

The Virtual Therapy Couch

I am aware of how absurd this all sounds. Artificial intelligence acting as a personal therapist? It’s the stuff that vibey A24 movies are made of. 

But interestingly, I am not alone in this experience. 

TikTok is awash with accounts of this newfound discovery—users hail it as the next best alternative in seeking mental health support. Stumbling upon their videos is how I wound up descending down this rabbit hole in the first place. 

One TikTok comment even declares: “ChatGPT is the best therapist ever.” 

A new study by Tebra , an operating system for independent healthcare providers, reveals that therapy AI chatbots are quickly currying favour. 

Among the 1,000 Americans and additional healthcare professionals surveyed, 1 in 4 Americans preferred AI chatbots over traditional therapy. Of those who turned to ChatGPT for advice, 80 percent found it a viable alternative. 

To be fair, we’ve been on this digital reliance for ages. Before ChatGPT, Google was our original sage. ChatGPT is merely the latest chapter in our ongoing dialogue with the cloud. 

My verdict? It’s like conversing with a therapist who somehow managed to devour every therapy book ever written, consumed all available data, and now knows all the right things I need or want to hear. 

Plus, it’s free, it’s accessible anytime, it’s non-judgemental.

The Threat to Therapists

It’s a ringing endorsement, if any. If you could have a free, 24/7 therapist that tucks neatly into your pocket, why bother blowing thousands of dollars on a real one? 

But Chirag Agarwal, co-owner of Talk Your Heart Out —an end-to-end therapy platform—tells me that the effectiveness of ChatGPT remains limited. 

“The prevailing misconception about therapy is that it should provide us with answers. In reality, therapy is more like holding up a mirror to our inner selves.”

Traditional therapy, in its essence, resembles a mental compass. It aims to assist patients in reconfiguring their thought processes, guiding them towards solutions that resonate with their personal values and desires. 

This demands a therapeutic relationship that is uniquely tailored to each individual’s specific circumstances, emotions, and history. ChatGPT, as an AI, struggles to pull off that level of individualised intimacy. 

It may be able to offer general information and support, but the algorithm is unable to follow up with the intensity of your emotional crisis. 

And unlike human therapists who can intuitively gauge when there’s more than what meets the eye, ChatGPT can’t scratch beneath the surface, or rather, go beyond what you explicitly express. And sooner or later, its responses start to become repetitive.

Easy Come, Easy Go

To be fair, the learning model warns you beforehand that (1) it isn’t a licensed therapist, and (2) its responses shouldn’t be considered a substitute for professional mental health advice. 

ChatGPT’s algorithm can’t fix your problems; it can only lend a digitised semblance of an ear. 

The language-based AI only feels so human because it has been trained (by humans) to do exactly that—sound human. 

It may give off the illusion of sentience. In truth, it’s just a machine excelling at its job scope. 

Meanwhile, articles warning about the pitfalls of ChatGPT aren’t scarce either— privacy concerns , bias , and safety issues are just the tip of the iceberg. After all, when you are navigating the tricky terrain of mental health, ethical considerations and safety should be top priority.

But, as the algorithm cautions, it isn’t a trained therapist. 

ChatGPT hasn’t been programmed to comply with the ethical and legal guidelines human therapists observe. For instance, it doesn’t understand the sanctity of patient-therapist confidentiality, a cornerstone of therapy. 

When you spill your deepest, darkest fears to ChatGPT, you can’t be entirely sure where that information will end up. Your secrets might as well be soaring through the digital cyberspace, potentially waiting for the world to see.

And somehow, the best and also most vexing part of ChatGPT is that it simply isn’t human. 

While ChatGPT works wonders at pulling data and reframing them into digestible natural language, the AI stumbles when discerning the nuances of human behaviour and language. 

The learning model is limited to only one form of communication: text. But text messaging can be flimsy . Effective communication depends not only on words but also on non-verbal body cues. 

Unfortunately, you can’t possibly emote for your computer. It can’t feel your frustration through the screen or instinctively know when that slight tremble in your voice equates to being on the brink of tears. 

Having to physically retype “I feel sad” into a chatbox can start to feel old after the first few times. Even worse, having to solemnly announce “I am crying” feels even more like an emotional buzzkill. 

Then, there’s also the linguistic minefield. To an AI, sarcasm might as well be interpreted as plain old seriousness. It can’t read the twinkle in your eye when you’re joking, and a deadpan remark might be mistaken for the real deal.

At Wit’s End

The shortfalls of ChatGPT are plenty. So why do some Singaporeans still turn to ChatGPT? 

Cost comes to mind—therapy is an expensive affair. 

We’re talking anywhere from $40 to $120 per session on the lower price range, with a one-hour dose of mental relief averaging around $60. If you’re in the market for an expert, be prepared to cough up a cool $200 or more for a single session. Imagine the bill it racks up after months or years in therapy.

Then there’s the fact that psychological services aren’t regulated in Singapore. This lack of oversight opens up the field to potential unethical practices and exploitative behaviours that, more often than not, go undetected. 

This hasn’t exactly been a boon for therapy’s reputation in Singapore, Chirag remarks. The absence of a proper licensing regime showcases a distressing undervaluation of the industry’s significance.

So, it’s not surprising that ChatGPT is appealing to Singaporeans. It bridges these gaps in our mental health scene while simultaneously dishing out all the features that we like. For free. 

Leticia*, who’s been in therapy for a year now, tells me that the financial aspect is the most lucrative factor.

“It all comes down to the cost. In-person therapy is just too financially draining.”

As someone who forks out $100 per session, she has to be prepared to set aside at least $350 a month. 

When costs are sky-high and the assurance of quality is uncertain, traditional therapy becomes more risky as compared to avenues such as ChatGPT.

“It makes sense why people go for ChatGPT. It’s free, so the stakes are low.” 

There’s also the matter of convenience—we’re a country of pragmatists after all. 

Who among us can spare the minutes to schedule an appointment, navigate the commute, and engage in hour-long heart-to-hearts? We don’t even delegate this much time for our lunch breaks sometimes. 

We’re a nation that prioritises speed; we want quick and efficient solutions to our problems. And who better to tackle this than ChatGPT, a pocket therapist we can whip out anytime, in the comfort of our own homes? 

Not to mention, you’re free to exit stage left at any time during a so-called session.

Faith* confesses that despite feeling content with traditional therapy, she still itches for the anonymity that ChatGPT grants. 

She recounts moments when she had to navigate numerous uncomfortable conversations with her family after news about her therapy broke. 

Never mind the number of times she reassures them of her well-being. The act of seeking therapy seemed to have instilled in them the belief that there must be something innately wrong .

“It was so awkward. It felt like they were walking on eggshells [around me]. They looked at me like I was somehow broken.” 

In a culture where self-reliance and saving face hold a premium, there remains an unwarranted stigma that clings to therapy. Opening up about your emotional struggles can, for some, be misconstrued as a sign of weakness or an inability to cope with life’s challenges.

When competitiveness runs deep, admitting that you’re struggling can feel akin to revealing a chink in your armour. 

On the flip side, ChatGPT offers a private escape route to share your deepest fears without the prying eyes of society. 

Who’s going to know if you start confiding in ChatGPT at 3 AM about relationship issues? 

An Inevitable Bandage

Faith likens ChatGPT to a metaphorical sounding board. It’s a good outlet for venting emotions—anger at your boss, sadness about a boyfriend, bitterness for that rude auntie who shoved you on the train. 

Still, she’s quick to interject that it shouldn’t be a complete replacement for traditional therapy. 

“It can be the appetiser and the dessert, but never the main course.” 

So, here’s the catch-22. Singaporeans want to get help , but are they equipped with the necessary means to? 

Faith and Leticia share that they are lucky that financial stability has afforded them the privilege of mental healthcare. What do we do for the countless others who aren’t as equally fortunate?

Chirag stresses that until the qualities of ChatGPT—affordable, accessible, and convenient—are reflected in our mental healthcare sphere, the AI model remains an inevitable fix.

Cautionary tales against using ChatGPT for therapy are aplenty. Still, I can’t guarantee I won’t be tempted to use it myself the next time life starts to go south.

And until my paycheck upgrades to a sizable amount where I can toss away hundreds a month, (regular) face-to-face therapy sessions might as well be an entry on a shopping list.

“Everyone needs support”, Chirag laments. 

“They will use whatever help they can get. How do we help them turn to the right one?” 

*Names have been changed to protect their identities

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OpenAI rolls out new features to ChatGPT’s DALL-E image generator

by Maria Deutscher

OpenAI today launched a new version of the DALL-E editor, the artificial intelligence image generator included in ChatGPT’s paid tiers.

The feature is based on an AI model called DALL-E 3 that the company debuted last September. A few weeks later, OpenAI integrated the model into ChatGPT. The original version of the DALL-E editor that launched last year enabled customers to generate images based on text prompts and visual examples, as well as make follow-up edits.

Today’s update will make it easier for users to edit the images they generate.

Within ChatGPT-3, the DALL-E editor is accessible through the same chatbot interface as the service’s other features. A newly added “Select” button at the top of the interface enables users to highlight the specific image section they wish to edit. From there, they can enter natural language instructions describing the changes they wish to make.

A user could, for example, draw a circle around a tree in a photo of a forest and have the DALL-E editor remove it. It’s also possible to change the design of the objects in an image or add new ones. “We recommend selecting a large space around the area you intend to edit to obtain better results,” OpenAI explained in a knowledge base article detailing the update.

The company’s engineers have also added a number of usability features on the occasion. In the DALL-E editor, new Undo and Redo buttons make it possible to quickly deselect sections of an image the user highlighted with the Select tool. Customers can also adjust the aspect ratio of the image that the tool generates, as well as access drawing style suggestions. 

The DALL-E editor is available in ChatGPT Pro, a paid edition of the chatbot geared towards consumers, as well as two more advanced product tiers that OpenAI offers for organizations. The feature is accessible in both the web and mobile versions.

DALL-E 3, the AI image generator on which the feature is based, is the third iteration of a neural network that OpenAI first debuted in 2021. It generates higher-quality images than the previous versions. It can also follow user instructions more accurately, a feature that OpenAI credits to DALL-E 3’s training dataset.

The company’s researchers trained the AI on a large collection of images and corresponding captions. According to OpenAI, 95% of those captions were created using a custom language model developed specifically for DALL-E 3. This language model generates relatively short image descriptions that only detail an image’s core elements, an approach OpenAI has found to be conducive to AI training.

DALL-E 3 is one of several models the company has developed for multimedia generation tasks. Its other entries into the category include Voice Engine , an AI system that can generate synthetic speech, and the Sora text-to-video model. DALL-E 3 is the only one of the three that OpenAI has made broadly available.

Image: OpenAI 

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Professor Silva’s chapter can be found online for free . "My Summer with ChatGPT," describes her early experiences with this increasingly popular technology and reviews an assignment that she has developed for her Academic Writing course.

She first learned about AI writing software in December of 2022 with the release of The Atlantic article, “The College Essay Is Dead.” In her own chapter, Professor Silva guides readers through personal experiences with the technology that has created controversy among educators. She concludes the chapter with an in-depth look at the ChatGPT-focused lesson plan she has created for students at Ithaca College.

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• How to Write an Essay with ChatGPT | Tips & Examples

How to Write an Essay with ChatGPT | Tips & Examples

Published on 26 June 2023 by Koen Driessen .

Passing off AI-generated text as your own work is widely considered plagiarism. However, when used correctly, generative AI tools like ChatGPT can legitimately help guide your writing process.

These tools are especially helpful in the preparation and revision stages of your essay writing.

You can use ChatGPT to:

• Write a research question
• Develop an outline
• Find relevant source information
• Summarise or paraphrase text
• Get feedback

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Table of contents

Writing a research question, developing an outline, finding source recommendations, summarising/paraphrasing text, getting feedback, other interesting articles, frequently asked questions.

You can use ChatGPT to brainstorm potential research questions or to narrow down your thesis statement . Begin by inputting a description of the research topic or assigned question. Then include a prompt like “Write 3 possible research questions on this topic”.

You can make the prompt as specific as you like. For example, you can include the writing level (e.g., high school essay, college essay), perspective (e.g., first person) and the type of essay you intend to write (e.g., argumentative , descriptive , expository , or narrative ).

You can also mention any facts or viewpoints you’ve gathered that should be incorporated into the output.

If the output doesn’t suit your topic, you can click “Regenerate response” to have the tool generate a new response. You can do this as many times as you like, and you can try making your prompt more specific if you struggle to get the results you want.

Example: Using ChatGPT to write a research question

• Generate three possible research questions for an argumentative high school essay on the following topic: “The long-term impact of the Covid-19 pandemic.”
• To what extent has the Covid-19 pandemic exacerbated existing social inequalities and what measures can be taken to address these disparities in the long term?
• How has the Covid-19 pandemic transformed education systems, and what are the potential long-term implications for students’ learning outcomes and educational equity?
• What lessons can be learned from the Covid-19 pandemic to enhance global preparedness and response to future public health crises, and how can these lessons be effectively implemented in the long term?
• Based on the second question, develop three further research questions with a specific focus on distance learning.
• What are the long-term effects of widespread adoption of distance learning during the Covid-19 pandemic on students’ academic performance, motivation, and overall educational outcomes?
• How has the shift to distance learning impacted the social and emotional well-being of students, and what strategies can be implemented to address any negative consequences in the long term?
• What are the key factors that contribute to the success of distance learning programs, and how can educational institutions and policymakers ensure equitable access to quality education in the long term, especially for marginalized and underserved student populations?

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Once you’ve decided on a research question, you can use ChatGPT to develop an essay outline . This can help keep you on track by giving you a clear idea of what topics you want to discuss and in what order.

Do this by prompting ChatGPT to create an outline for a specific research question, mentioning any topics or points you want to discuss. You can also mention the writing level and the intended length of your essay so that the tool generates an appropriate outline.

You can then refine this by further prompting ChatGPT or editing the outline manually until it meets your requirements.

Examples: Using ChatGPT to generate an essay outline

• Develop an outline for an argumentative high school essay with the following research question: “What are the long-term effects of widespread adoption of distance learning during the Covid-19 pandemic on students’ academic performance and overall educational outcomes?”The essay will be about 4 pages long.
• Background information on the Covid-19 pandemic and the shift to distance learning
• Thesis statement: The widespread adoption of distance learning during the Covid-19 pandemic has had both positive and negative long-term effects on students’ academic performance and overall educational outcomes.
• Increased flexibility and personalized learning opportunities
• Access to a wider range of resources and educational materials
• Development of self-directed learning skills
• Critiques and limitations of previous studies
• Lack of face-to-face interaction and reduced social engagement
• Increased potential for distractions and lack of structure
• Technological barriers and unequal access to resources
• Examination of studies and data on academic achievement during distance learning
• Analysis of long-term effects on educational attainment and future opportunities
• Discussion of potential implications for college admissions and career readiness
• Strategies for promoting student engagement and motivation in distance learning
• Importance of teacher-student communication and support
• Addressing technological disparities and ensuring equitable access to resources
• Recap of the main points discussed
• Emphasize the need for further research and long-term planning to optimize distance learning experiences
• Restate the thesis and provide a final thought on the potential long-term effects of distance learning on students’ academic performance and overall educational outcomes

Once you know the scope of your essay, you can find relevant primary and secondary sources to support your argument.

However, we don’t recommend prompting ChatGPT to generate a list of sources as it occasionally makes mistakes (like listing nonexistent sources). Instead, it’s a good idea to use ChatGPT to get suggestions for the types of sources relevant to your essay and track them down using a credible research database or your institution’s library.

When you have found relevant sources, use a specialised tool like the Scribbr Citation Generator to cite them in your essay.

Example: Using ChatGPT to find sources

• What types of sources can I use to write an essay on the following research question?”What are the long-term effects of widespread adoption of distance learning during the Covid-19 pandemic on students’ academic performance and overall educational outcomes?”
• Academic Journals and Research Articles: Look for scholarly articles and research studies published in peer-reviewed academic journals. These sources often provide in-depth analysis, empirical evidence, and statistical data on the long-term effects of distance learning.
• Educational and Government Reports: Reports published by educational institutions, government agencies, or educational organizations can offer valuable insights into the impact of distance learning on academic performance and educational outcomes. These reports may include surveys, case studies, and data analysis specific to the Covid-19 pandemic and distance learning.
• Official Websites and Publications: Explore the official websites of educational institutions, education ministries, or educational technology companies. They often publish articles, white papers, or research findings related to distance learning and its effects on students’ academic performance.
• Expert Interviews or Opinion Pieces: Seek out interviews or opinion pieces from experts in the field of education, distance learning, or pedagogy. These sources can provide valuable perspectives and insights on the long-term effects of distance learning on students’ educational outcomes.
• Online Databases and Digital Libraries: Utilize online databases and digital libraries such as JSTOR, Google Scholar, or academic databases provided by your educational institution. These platforms offer a wide range of scholarly articles, research papers, and publications that can support your arguments.
• Surveys and Studies Conducted by Educational Institutions: Some schools or universities may have conducted their own surveys or studies to evaluate the impact of distance learning on students. Check if your educational institution has published any research findings or survey results that are relevant to your topic.

During your writing process, you can use ChatGPT as a summarising tool to condense text to its essential ideas or as a paraphraser to rephrase text in clear, accessible language. Using ChatGPT in these ways can help you to understand complex material, express your own ideas more clearly, and avoid repetition.

Simply input the relevant text and prompt the tool to summarise or paraphrase it. Alternatively, you can use Scribbr’s free text summariser and Scribbr’s free paraphrasing tool , which are specifically designed for these purposes.

Example: Using ChatGPT to summarise/paraphrase

• Paraphrase the following text using clear language:”Engaging in an extensive examination of the intricate interplay between remote instructional methods and academic achievement necessitates consideration of diverse factors, including the pedagogical efficacy of virtual platforms, the socioemotional ramifications of reduced face-to-face interactions, the equitable accessibility of technological resources, and the potential long-lasting implications for students’ educational trajectories and future opportunities.”
• To thoroughly explore the connection between remote teaching methods and academic success, it is crucial to take into account various factors. These factors include evaluating how well virtual platforms support effective teaching, understanding the impact of limited in-person interactions on students’ emotional well-being, ensuring fair access to technology resources, and recognizing the potential long-term effects on students’ educational paths and future prospects.

Once you’ve written your essay, you can prompt ChatGPT to provide feedback and recommend improvements.

You can indicate how the tool should provide feedback (e.g., “Act like a university professor examining papers”) and include the specific points you want to receive feedback on (e.g., consistency of tone, clarity of argument, appropriateness of evidence).

While this is not an adequate substitute for an experienced academic supervisor, it can help you with quick preliminary feedback.

You can also use ChatGPT to check grammar mistakes. However, ChatGPT sometimes misses errors and on rare occasions may even introduce new grammatical mistakes. We suggest using a tool like Scribbr’s free grammar checker , which is designed specifically for this purpose. Or, for more in-depth feedback, try Scribbr’s proofreading and editing service .

Example: Using ChatGPT for feedback

• Feedback the above text with regard to consistency of tone, clarity of argument, appropriateness of evidence, and coherence of structure.
• Clarity of Argument: The argument could be further clarified by explicitly connecting the discussed factors to their impact on academic performance. For example, explicitly stating how the proficiency of virtual platforms and equitable access to technology affect academic performance would strengthen the clarity of the argument.
• Appropriateness of Evidence: The evidence provided is generally appropriate and relevant. To enhance the strength of the evidence, consider incorporating specific examples, research studies, or statistics that directly support the discussed factors and their impact on academic performance. This would provide stronger evidence for the claims made.
• Coherence of Structure: The structure of the text is coherent, with a clear introduction, body paragraphs addressing different factors, and a conclusion. To improve coherence, consider adding transitional phrases or sentences at the beginning or end of paragraphs to facilitate the smooth flow of ideas between paragraphs. This would enhance the overall structure and make it easier for the reader to follow the argument.

Overall, the text demonstrates a consistent tone, a clear argument, appropriate evidence, and a coherent structure. Clarifying the argument by explicitly connecting the factors to their impact, incorporating stronger evidence, and adding transitional phrases for better coherence would further enhance the text’s effectiveness. Note Passing off AI-generated text as your own work is generally considered plagiarism (or at least academic dishonesty ) and may result in an automatic fail and other negative consequences . AI detectors may be used to detect this offence.

If you want more tips on using AI tools , understanding plagiarism , and citing sources , make sure to check out some of our other articles with explanations, examples, and formats.

• Citing ChatGPT
• Best grammar checker
• Best paraphrasing tool
• ChatGPT in your studies
• Is ChatGPT trustworthy?
• Types of plagiarism
• Self-plagiarism
• Avoiding plagiarism
• Academic integrity
• Best plagiarism checker

Citing sources

• Citation styles
• In-text citation
• Citation examples
• Annotated bibliography

Yes, you can use ChatGPT to summarise text . This can help you understand complex information more easily, summarise the central argument of your own paper, or clarify your research question.

You can also use Scribbr’s free text summariser , which is designed specifically for this purpose.

Yes, you can use ChatGPT to paraphrase text to help you express your ideas more clearly, explore different ways of phrasing your arguments, and avoid repetition.

However, it’s not specifically designed for this purpose. We recommend using a specialised tool like Scribbr’s free paraphrasing tool , which will provide a smoother user experience.

Using AI writing tools (like ChatGPT ) to write your essay is usually considered plagiarism and may result in penalisation, unless it is allowed by your university. Text generated by AI tools is based on existing texts and therefore cannot provide unique insights. Furthermore, these outputs sometimes contain factual inaccuracies or grammar mistakes.

However, AI writing tools can be used effectively as a source of feedback and inspiration for your writing (e.g., to generate research questions ). Other AI tools, like grammar checkers, can help identify and eliminate grammar and punctuation mistakes to enhance your writing.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Driessen, K. (2023, June 26). How to Write an Essay with ChatGPT | Tips & Examples. Scribbr. Retrieved 6 May 2024, from https://www.scribbr.co.uk/using-ai-tools/chatgpt-essays/

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• Published: 01 May 2024

A critical assessment of using ChatGPT for extracting structured data from clinical notes

• Jingwei Huang   ORCID: orcid.org/0000-0003-2155-6107 1 ,
• Donghan M. Yang 1 ,
• Ruichen Rong 1 ,
• Kuroush Nezafati   ORCID: orcid.org/0000-0002-6785-7362 1 ,
• Colin Treager 1 ,
• Zhikai Chi   ORCID: orcid.org/0000-0002-3601-3351 2 ,
• Shidan Wang   ORCID: orcid.org/0000-0002-0001-3261 1 ,
• Xian Cheng 1 ,
• Yujia Guo 1 ,
• Laura J. Klesse 3 ,
• Guanghua Xiao 1 ,
• Eric D. Peterson 4 ,
• Xiaowei Zhan 1 &
• Yang Xie   ORCID: orcid.org/0000-0001-9456-1762 1  

npj Digital Medicine volume  7 , Article number:  106 ( 2024 ) Cite this article

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• Non-small-cell lung cancer

Existing natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT’s capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral “prompt engineering” process, leveraging OpenAI’s API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.

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Introduction.

Large Language Models (LLMs) 1 , 2 , 3 , 4 , 5 , 6 , such as Generative Pre-trained Transformer (GPT) models represented by ChatGPT, are being utilized for diverse applications across various sectors. In the healthcare industry, early applications of LLMs are being used to facilitate patient-clinician communication 7 , 8 . To date, few studies have examined the potential of LLMs in reading and interpreting clinical notes, turning unstructured texts into structured, analyzable data.

Traditionally, the automated extraction of structured data elements from medical notes has relied on medical natural language processing (NLP) using rule-based or machine-learning approaches or a combination of both 9 , 10 . Machine learning methods 11 , 12 , 13 , 14 , particularly deep learning, typically employ neural networks and the first generation of transformer-based large language models (e.g., BERT). Medical domain knowledge needs to be integrated into model designs to enhance performance. However, a significant obstacle to developing these traditional medical NLP algorithms is the limited existence of human-annotated datasets and the costs associated with new human annotation 15 . Despite meticulous ground-truth labeling, the relatively small corpus sizes often result in models with poor generalizability or make evaluations of generalizability impossible. For decades, conventional artificial intelligence (AI) systems (symbolic and neural networks) have suffered from a lack of general knowledge and commonsense reasoning. LLMs, like GPT, offer a promising alternative, potentially using commonsense reasoning and broad general knowledge to facilitate language processing.

ChatGPT is the application interface of the GPT model family. This study explores an approach to using ChatGPT to extract structured data elements from unstructured clinical notes. In this study, we selected lung cancer pathology reports as the corpus for extracting detailed diagnosis information for lung cancer. To accomplish this, we developed and improved a prompt engineering process. We then evaluated the effectiveness of this method by comparing the ChatGPT output with expert-curated structured data and used case studies to provide insights into how ChatGPT read and interpreted notes and why it made mistakes in some cases.

Data and endpoints

The primary objective of this study was to develop an algorithm and assess the capabilities of ChatGPT in processing and interpreting a large volume of free-text clinical notes. To evaluate this, we utilized unstructured lung cancer pathology notes, which provide diagnostic information essential for developing treatment plans and play vital roles in clinical and translational research. We accessed a total of 1026 lung cancer pathology reports from two web portals: the Cancer Digital Slide Archive (CDSA data) ( https://cancer.digitalslidearchive.org/ ) and The Cancer Genome Atlas (TCGA data) ( https://cBioPortal.org ). These platforms serve as public data repositories for de-identified patient information, facilitating cancer research. The CDSA dataset was utilized as the “training” data for prompt development, while the TCGA dataset, after removing the overlapping cases with CDSA, served as the test data for evaluating the ChatGPT model performance.

From all the downloaded 99 pathology reports from CDSA for the training data, we excluded 21 invalid reports due to near-empty content, poor scanning quality, or missing report forms. Seventy-eight valid pathology reports were included as the training data to optimize the prompt. To evaluate the model performance, 1024 pathology reports were downloaded from cBioPortal. Among them, 97 overlapped with the training data and were excluded from the evaluation. We further excluded 153 invalid reports due to near-empty content, poor scanning quality, or missing report forms. The invalid reports were preserved to evaluate ChatGPT’s handling of irregular inputs separately, and were not included in the testing data for accuracy performance assessment. As a result, 774 valid pathology reports were included as the testing data for performance evaluation. These valid reports still contain typos, missing words, random characters, incomplete contents, and other quality issues challenging human reading. The corresponding numbers of reports used at each step of the process are detailed in Fig. 1 .

Exclusions are accounted for due to reasons such as empty reports, poor scanning quality, and other factors, including reports of stage IV or unknown conditions.

The specific task of this study was to identify tumor staging and histology types which are important for clinical care and research from pathology reports. The TNM staging system 16 , outlining the primary tumor features (T), regional lymph node involvement (N), and distant metastases (M), is commonly used to define the disease extent, assign prognosis, and guide lung cancer treatment. The American Joint Committee on Cancer (AJCC) has periodically released various editions 16 of TNM classification/staging for lung cancers based on recommendations from extensive database analyses. Following the AJCC guideline, individual pathologic T, N, and M stage components can be summarized into an overall pathologic staging score of Stage I, II, III, or IV. For this project, we instructed ChatGPT to use the AJCC 7 th edition Cancer Staging Manual 17 as the reference for staging lung cancer cases. As the lung cancer cases in our dataset are predominantly non-metastatic, the pathologic metastasis (pM) stage was not extracted. The data elements we chose to extract and evaluate for this study are pathologic primary tumor (pT) and pathologic lymph node (pN) stage components, overall pathologic tumor stage, and histology type.

Overall Performance

Using the training data in the CDSA dataset ( n  = 78), we experimented and improved prompts iteratively, and the final prompt is presented in Fig. 2 . The overall performance of the ChatGPT (gpt-3.5-turbo-16k model) is evaluated in the TCGA dataset ( n  = 774), and the results are summarized in Table 1 . The accuracy of primary tumor features (pT), regional lymph node involvement (pN), overall tumor stage, and histological diagnosis are 0.87, 0.91, 0.76, and 0.99, respectively. The average accuracy of all attributes is 0.89. The coverage rates for pT, pN, overall stage and histological diagnosis are 0.97, 0.94, 0.94 and 0.96, respectively. Further details of the accuracy evaluation, F1, Kappa, recall, and precision for each attribute are summarized as confusion matrices in Fig. 3 .

Final prompt for information extraction and estimation from pathology reports.

For meaningful evaluation, the cases with uncertain values, such as “Not Available”, “Not Specified”, “Cannot be determined”, “Unknown”, et al. in reference and prediction have been removed. a Primary tumor features (pT), b regional lymph node involvement (pN), c overall tumor stage, and d histological diagnosis.

Inference and Interpretation

To understand how ChatGPT reads and makes inferences from pathology reports, we demonstrated a case study using a typical pathology report in this cohort (TCGA-98-A53A) in Fig. 4a . The left panel shows part of the original pathology report, and the right panel shows the ChatGPT output with estimated pT, pN, overall stage, and histology diagnosis. For each estimate, ChatGPT gives the confidence level and the corresponding evidence it used for the estimation. In this case, ChatGPT correctly extracted information related to tumor size, tumor features, lymph node involvement, and histology information and used the AJCC staging guidelines to estimate tumor stage correctly. In addition, the confidence level, evidence interpretation, and case summary align well with the report and pathologists’ evaluations. For example, the evidence for the pT category was described as “The pathology report states that the tumor is > 3 cm and < 5 cm in greatest dimension, surrounded by lung or visceral pleura.” The evidence for tumor stage was described as “Based on the estimated pT category (T2a) and pN category (N0), the tumor stage is determined to be Stage IB according to AJCC7 criteria.” It shows that ChatGPT extracted relevant information from the note and correctly inferred the pT category based on the AJCC guideline (Supplementary Fig. 1 ) and the extracted information.

a TCGA-98-A53A. An example of a scanned pathological report (left panel) and ChatGPT output and interpretation (right panel). All estimations and support evidence are consistent with the pathologist’s evaluations. b The GPT model correctly inferred pT as T2a based on the tumor’s size and involvement according to AJCC guidelines.

In another more complex case, TCGA-50-6590 (Fig. 4b ), ChatGPT correctly inferred pT as T2a based on both the tumor’s size and location according to AJCC guidelines. Case TCGA-44-2656 demonstrates a more challenging scenario (Supplementary Fig. 2 ), where the report only contains some factual data without specifying pT, pN, and tumor stage. However, ChatGPT was able to infer the correct classifications based on the reported facts and provide proper supporting evidence.

Error analysis

To understand the types and potential reasons for misclassifications, we performed a detailed error analysis by looking into individual attributes and cases where ChatGPT made mistakes, the results of which are summarized below.

Primary tumor feature (pT) classification

In total, 768 cases with valid reports and reference values in the testing data were used to evaluate the classification performance of pT. Among them, 15 cases were reported with unknown or empty output by ChatGPT, making the coverage rate 0.97. For the remaining 753 cases, 12.6% of pT was misclassified. Among these misclassification cases, the majority were T1 misclassified as T2 (67 out of 753 or 8.9%) or T3 misclassified as T2 (12 out of 753, or 1.6%).

In most cases, ChatGPT extracted the correct tumor size information but used an incorrect rule to distinguish pT categories. For example, in the case TCGA-22-4609 (Fig. 5a ), ChatGPT stated, “Based on the tumor size of 2.0 cm, it falls within the range of T2 category according to AJCC 7th edition for lung carcinoma staging manual.” However, according to the AJCC 7 th edition staging guidelines for lung cancer, if the tumor is more than 2 cm but less than 3 cm in greatest dimension and does not invade nearby structures, pT should be classified as T1b. Therefore, ChatGPT correctly extracted the maximum tumor dimension of 2 cm but incorrectly interpreted this as meeting the criteria for classification as T2. Similarly, for case TCGA-85-A4JB, ChatGPT incorrectly claimed, “Based on the tumor size of 10 cm, the estimated pT category is T2 according to AJCC 7th edition for lung carcinoma staging manual.” According to the AJCC 7 th edition staging guidelines, a tumor more than 7 cm in greatest dimension should be classified as T3.

a TCGA-22-4609 illustrates a typical case where the GPT model uses a false rule, which is incorrect by AJCC guideline. b Case TCGA-39-5028 shows a complex case where there exist two tumors and the GPT model only capture one of them. c Case TCGA-39-5016 reveals a case where the GPT model made a mistake for getting confused with domain terminology.

Another challenging situation arose when multiple tumor nodules were identified within the lung. In the case of TCGA-39-5028 (Fig. 5b ), two separate tumor nodules were identified: one in the right upper lobe measuring 2.1 cm in greatest dimension and one in the right lower lobe measuring 6.6 cm in greatest dimension. According to the AJCC 7 th edition guidelines, the presence of separate tumor nodules in a different ipsilateral lobe results in a classification of T4. However, ChatGPT classified this case as T2a, stating, “The pathology report states the tumor’s greatest diameter as 2.1 cm”. This classification would be appropriated if the right upper lobe nodule were a single isolated tumor. However, ChatGPT failed to consider the presence of the second, larger nodule in the right lower lobe when determining the pT classification.

Regional lymph node involvement (pN)

The classification performance of pN was evaluated using 753 cases with valid reports and reference values in the testing data. Among them, 27 cases were reported with unknown or empty output by ChatGPT, making the coverage rate 0.94. For the remaining 726 cases, 8.5% of pN was misclassified. Most of these misclassification cases were N1 misclassified as N2 (32 cases). The AJCC 7th edition staging guidelines use the anatomic locations of positive lymph nodes to determine N1 vs. N2. However, most of the misclassification cases were caused by ChatGPT interpreting the number of positive nodes rather than the locations of the positive nodes. One such example is the case TCGA-85-6798. The report states, “Lymph nodes: 2/16 positive for metastasis (Hilar 2/16)”. Positive hilar lymph nodes correspond to N1 classification according to AJCC 7th edition guidelines. However, ChatGPT misclassifies this case as N2, stating, “The pathology report states that 2 out of 16 lymph nodes are positive for metastasis. Based on this information, the pN category can be estimated as N2 according to AJCC 7th edition for lung carcinoma staging manual.” This interpretation is incorrect, as the number of positive lymph nodes is not part of the criteria used to determine pN status according to AJCC 7th edition guidelines. The model misinterpreted pN2 predictions in 22 cases due to similar false assertions.

In some cases, the ChatGPT model made classification mistakes by misunderstanding the locations’ terminology. Figure 5c shows a case (TCGA-39-5016) where the ChatGPT model recognized that “6/9 peribronchial lymph nodes involved, “ corresponding with classification as N1, but ChatGPT misclassified this case as N2. By AJCC 7th edition guidelines, N2 is defined as “Metastasis in ipsilateral mediastinal and/or subcarinal lymph node(s)”. The ChatGPT model did not fully understand that terminology and made misclassifications.

Pathology tumor stage

The overall tumor stage classification performance was evaluated using 744 cases with valid reports and reference values as stage I, II and III in the testing data. Among them, 18 cases were reported as unknown or empty output by ChatGPT making the coverage rate as 0.94. For the remaining 726 cases, 23.6% of the overall stage was misclassified. Since the overall stage depends on individual pT and pN stages, the mistakes could come from misclassification of pT or pN (error propagation) or applying incorrect inference rules to determine the overall stage from pT and pN (incorrect rules). Looking into the 56 cases where ChatGPT misclassified stage II as stage III, 22 cases were due to error propagation, and 34 were due to incorrect rules. Figure 6a shows an example of error propagation (TCGA-MP-A4TK). ChatGPT misclassified the pT stage from T2a to T3, and then this mistake led to the incorrect classification of stage IIA to stage IIIA. Figure 6b illustrates a case (TCGA-49-4505) where ChatGPT made correct estimation of pT and pN but made false prediction about tumor stage by using a false rule. Among the 34 cases affected by incorrect rules, ChatGPT mistakenly inferred tumor stage as stage III for 26 cases where pT is T3 and pN is N0, respectively. For example, for case TCGA-55-7994, ChatGPT provided the evidence as “Based on the estimated pT category (T3) and pN category (N0), the tumor stage is determined to be Stage IIIA according to AJCC7 criteria”. According to AJCC7, tumors with T3 and N0 should be classified as stage IIB. Similarly, error analysis for other tumor stages shows that misclassifications come from both error propagation and applying false rules.

a Case TCGA-MP-A4TK: An example of typical errors GPT made in the experiments, i.e. GPT took false rule and further led to faulty propagation. b Case TCGA-49-4505: The GPT model made false estimation of Stage IIIA with a false rule, although it made correct inference with T2b and N1.

Histological diagnosis

The classification performance of histology diagnosis was evaluated using 762 cases with valid reports and reference values in the testing data. Among them, 17 cases were reported as either unknown or empty output by ChatGPT, making the coverage rate 0.96. For the remaining 745 cases, 6 ( < 1%) of histology types were misclassified. Among the mistakes that ChatGPT made for histology diagnosis, ChatGPT misclassified 3 of them as “other” type and 3 cases of actual “other” type (neither adenocarcinomas nor squamous cell carcinomas) as 2 adenocarcinomas and 1 squamous cell carcinoma. In TCGA-22-5485, two tumors exist: one squamous cell carcinoma and another adenocarcinoma, which should be classified as the ‘other’ type. However, ChatGPT only identified and extracted information for one tumor. In the case TCGA-33-AASB, which is the “other” type of histology, ChatGPT captured the key information and gave it as evidence: “The pathology report states the histologic diagnosis as infiltrating poorly differentiated non-small cell carcinoma with both squamous and glandular features”. However, it mistakenly estimated this case as “adenocarcinoma”. In another case (TCGA-86-8668) of adenocarcinoma, ChatGPT again captured key information and stated as evidence, “The pathology report states the histologic diagnosis as Bronchiolo-alveolar carcinoma, mucinous” but could not tell it is a subtype of adenocarcinoma. Both cases reveal that ChatGPT still has limitations in the specific domain knowledge in lung cancer pathology and the capability of correcting understanding its terminology.

Analyzing irregularities

The initial model evaluation and prompt-response review uncovered irregular scenarios: the original pathology reports may be blank, poorly scanned, or simply missing report forms. We reviewed how ChatGPT responded to these anomalies. First, when a report was blank, the prompt contained only the instruction part. ChatGPT failed to recognize this situation in most cases and inappropriately generated a fabricated case. Our experiments showed that, with the temperature set at 0 for blank reports, ChatGPT converged to a consistent, hallucinated response. Second, for nearly blank reports with a few random characters and poorly scanned reports, ChatGPT consistently converged to the same response with increased variance as noise increased. In some cases, ChatGPT responded appropriately to all required attributes but with unknown values for missing information. Last, among the 15 missing report forms in a small dataset, ChatGPT responded “unknown” as expected in only 5 cases, with the remaining 10 still converging to the hallucinated response.

Reproducibility evaluation

Since ChatGPT models (even with the same version) evolve over time, it is important to evaluate the stability and reproducibility of ChatGPT. For this purpose, we conducted experiments with the same model (“gpt-3.5-turbo-0301”), the same data, prompt, and settings (e.g., temperature = 0) twice in early April and the middle of May of 2023. The rate of equivalence between ChatGPT estimations in April and May on key attributes of interest (pT, pN, tumor stage, and histological diagnosis) is 0.913. The mean absolute error between certainty degrees in the two experiments is 0.051. Considering the evolutionary nature of ChatGPT models, we regard an output difference to a certain extent as reasonable and the overall ChatGPT 3.5 model as stable.

Comparison with other NLP methods

In order to have a clear perspective on how ChatGPT’s performance stands relative to established methods, we conducted a comparative analysis of the results generated by ChatGPT with two established methods: a keyword search algorithm and a deep learning-based Named Entity Recognition (NER) method.

Data selection and annotation

Since the keyword search and NER methods do not support zero-shot learning and require human annotations on the entity level, we carefully annotated our dataset for these traditional NLP methods. We used the same training and testing datasets as in the prompt engineering for ChatGPT. The training dataset underwent meticulous annotation by experienced medical professionals, adhering to the AJCC7 standards. This annotation process involved identifying and highlighting all relevant entities and text spans related to stage, histology, pN, and pT attributes. The detailed annotation process for the 78 cases required a few weeks of full-time work from medical professionals.

Keyword search algorithm using wordpiece tokenizer

For the keyword search algorithm, we employed the WordPiece tokenizer to segment words into subwords. We compiled an annotated entity dictionary from the training dataset. To assess the performance of this method, we calculated span similarities between the extracted spans in the validation and testing datasets and the entries in the dictionary.

Named Entity Recognition (NER) classification algorithm

For the NER classification algorithm, we designed a multi-label span classification model. This model utilized the pre-trained Bio_ClinicalBERT as its backbone. To adapt it for multi-label classification, we introduced an additional linear layer. The model underwent fine-tuning for 1000 epochs using the stochastic gradient descent (SGD) optimizer. The model exhibiting the highest overall F1 score on the validation dataset was selected as the final model for further evaluation in the testing dataset.

Performance evaluation

We evaluated the performance of both the keyword search and NER methods on the testing dataset. We summarized the predicted entities/spans and their corresponding labels. In cases where multiple related entities were identified for a specific category, we selected the most severe entities as the final prediction. Moreover, we inferred the stage information for corpora lacking explicit staging information by aggregating details from pN, pT, and diagnosis, aligning with the AJCC7 protocol. The overall predictions for stage, diagnosis, pN, and pT were compared against the ground truth table to gauge the accuracy and effectiveness of our methods. The results (Supplementary Table S1 ) show that the ChatGPT outperforms WordPiece tokenizer and NER Classifier. The average accuracy for ChatGPT, WordPiece tokenizer, and NER Classifier are 0.89, 0.51, and 0.76, respectively.

Prompt engineering process and results

Prompt design is a heuristic search process with many elements to consider, thus having a significantly large design space. We conducted many experiments to explore better prompts. Here, we share a few typical prompts and the performance of these prompts in the training data set to demonstrate our prompt engineering process.

Output format

The most straightforward prompt without special design would be: “read the pathology report and answer what are pT, pN, tumor stage, and histological diagnosis”. However, this simple prompt would make ChatGPT produce unstructured answers varying in format, terminology, and granularity across the large number of pathology reports. For example, ChatGPT may output pT as “T2” or “pT2NOMx”, and it outputs histological diagnosis as “Multifocal invasive moderately differentiated non-keratinizing squamous cell carcinoma”. The free-text answers will require a significant human workload to clean and process the output from ChatGPT. To solve this problem, we used a multiple choice answer format to force ChatGPT to pick standardized values for some attributes. For example, for pT, ChatGPT could only provide the following outputs: “T0, Tis, T1, T1a, T1b, T2, T2a, T2b, T3, T4, TX, Unknown”. For the histologic diagnosis, ChatGPT could provide output in one of these categories: Lung Adenocarcinoma, Lung Squamous Cell Carcinoma, Other, Unknown. In addition, we added the instruction, “Please make sure to output the whole set of answers together as a single JSON file, and don’t output anything beyond the required JSON file,” to emphasize the requirement for the output format. These requests in the prompt make the downstream analysis of ChatGPT output much more efficient. In order to know the certainty degree of ChatGPT’s estimate and the evidence, we asked ChatGPT to provide the following 4 outputs for each attribute/variable: extracted value as stated in the pathology report, estimated value based on AJCC 7th edition for lung carcinoma staging manual, the certainty degree of the estimation, and the supporting evidence for the estimation. The classification accuracy of this prompt with multiple choice output format (prompt v1) in our training data could achieve 0.854.

Evidence-based inference

One of the major concerns for LLM is that the results from the model are not supported by any evidence, especially when there is not enough information for specific questions. In order to reduce this problem, we emphasize the use of evidence for inference in the prompt by adding this instruction to ChatGPT: “Please ensure to make valid inferences for attribute estimation based on evidence. If there is no available evidence provided to make an estimation, please answer the value as “Unknown.” In addition, we asked ChatGPT to “Include “comment” as the last key of the JSON file.” After adding these two instructions (prompt v2), the performance of the classification in the training data increased to 0.865.

Chain of thought prompting by asking intermediate questions

Although tumor size is not a primary interest for diagnosis and clinical research, it plays a critical role in classifying the pT stage. We hypothesize that if ChatGPT pays closer attention to tumor size, it will have better classification performance. Therefore, we added an instruction in the prompt (prompt v3) to ask ChatGPT to estimate: “tumor size max_dimension: [<the greatest dimension of tumor in Centimeters (cm)>, ‘Unknown’]” as one of the attributes. After this modification, the performance of the classification in the training data increased to 0.90.

Providing examples

Providing examples is an effective way for humans to learn, and it should have similar effects for ChatGPT. We provided a specific example to infer the overall stage based on pT and pN by adding this instruction: “Please estimate the tumor stage category based on your estimated pT category and pN category and use AJCC7 criteria. For example, if pT is estimated as T2a and pN as N0, without information showing distant metastasis, then by AJCC7 criteria, the tumor stage is “Stage IB”.” After this modification (prompt v4), the performance of the classification in the training data increased to 0.936.

Although we can further refine and improve prompts, we decided to use prompt v4 as the final model and apply it to the testing data and get the final classification accuracy of 0.89 in the testing data.

ChatGPT-4 performance

LLM evolves rapidly and OpenAI just released the newest GPT-4 Turbo model (GPT-4-1106-preview) in November 2023. To compare this new model with GPT-3.5-Turbo, we applied this newest GPT model GPT-4-1106 to analyze all the lung cancer pathology notes in the testing data. The classification result and the comparison with the GPT-3.5-Turbo-16k are summarized in Supplementary Table 1 . The results show that GPT-4-turbo performs better in almost every aspect; overall, the GPT-4-turbo model increases performance by over 5%. However, GPT-4-Turbo is much more expensive than GPT-3.5-Turbo. The performance of GPT-3.5-Turbo-16k is still comparable and acceptable. As such, this study mainly focuses on assessing GPT-3.5-Turbo-16k, but highlights the fast development and promise of using LLM to extract structured data from clinical notes.

Analyzing osteosarcoma data

To demonstrate the broader application of this method beyond lung cancer, we collected and analyzed clinical notes from pediatric osteosarcoma patients. Osteosarcoma, the most common type of bone cancer in children and adolescents, has seen no substantial improvement in patient outcomes for the past few decades 18 . Histology grades and margin status are among the most important prognostic factors for osteosarcoma. We collected pathology reports from 191 osteosarcoma cases (approved by UTSW IRB #STU 012018-061). Out of these, 148 cases had histology grade information, and 81 had margin status information; these cases were used to evaluate the performance of the GPT-3.5-Turbo-16K model and our prompt engineering strategy. Final diagnoses on grade and margin were manually reviewed and curated by human experts, and these diagnoses were used to assess ChatGPT’s performance. All notes were de-identified prior to analysis. We applied the same prompt engineering strategy to extract grade and margin information from these osteosarcoma pathology reports. This analysis was conducted on our institution’s private Azure OpenAI platform, using the GPT-3.5-Turbo-16K model (version 0613), the same model used for lung cancer cases. ChatGPT accurately classified both grades (with a 98.6% accuracy rate) and margin status (100% accuracy), as shown in Supplementary Fig. 3 . In addition, Supplementary Fig. 4 details a specific case, illustrating how ChatGPT identifies grades and margin status from osteosarcoma pathology reports.

Since ChatGPT’s release in November 2022, it has spurred many potential innovative applications in healthcare 19 , 20 , 21 , 22 , 23 . To our knowledge, this is among the first reports of an end-to-end data science workflow for prompt engineering, using, and rigorously evaluating ChatGPT in its capacity of batch-processing information extraction tasks on large-scale clinical report data.

The main obstacle to developing traditional medical NLP algorithms is the limited availability of annotated data and the costs for new human annotations. To overcome these hurdles, particularly in integrating problem-specific information and domain knowledge with LLMs’ task-agnostic general knowledge, Augmented Language Models (ALMs) 24 , which incorporate reasoning and external tools for interaction with the environment, are emerging. Research shows that in-context learning (most influentially, few-shot prompting) can complement LLMs with task-specific knowledge to perform downstream tasks effectively 24 , 25 . In-context learning is an approach of training through instruction or light tutorial with a few examples (so called few-shot prompting; well instruction without any example is called 0-shot prompting) rather than fine-tuning or computing-intensive training, which adjusts model weights. This approach has become a dominant method for using LLMs in real-world problem-solving 24 , 25 , 26 . The advent of ALMs promises to revolutionize almost every aspect of human society, including the medical and healthcare domains, altering how we live, work, and communicate. Our study shows the feasibility of using ChatGPT to extract data from free text without extensive task-specific human annotation and model training.

In medical data extraction, our study has demonstrated the advantages of adopting ChatGPT over traditional methods in terms of cost-effectiveness and efficiency. Traditional approaches often require labor-intensive annotation processes that may take weeks and months from medical professionals, while ChatGPT models can be fine-tuned for data extraction within days, significantly reducing the time investment required for implementation. Moreover, our economic analysis revealed the cost savings associated with using ChatGPT, with processing over 900 pathology reports incurring a minimal monetary cost (less than $10 using GPT 3.5 Turbo and less than $30 using GPT-4 Turbo). This finding underscores the potential benefits of incorporating ChatGPT into medical data extraction workflows, not only for its time efficiency but also for its cost-effectiveness, making it a compelling option for medical institutions and researchers seeking to streamline their data extraction processes without compromising accuracy or quality.

A critical requirement for effectively utilizing an LLM is crafting a high-quality “prompt” to instruct the LLM, which has led to the emergence of an important methodology referred to as “prompt engineering.” Two fundamental principles guide this process: firstly, the provision of appropriate context, and secondly, delivering clear instructions about subtasks and the requirements for the desired response and how it should be presented. For a single query for one-time use, the user can experiment with and revise the prompt within the conversation session until a satisfactory answer is obtained. However, prompt design can become more complex when handling repetitive tasks over many input data files using the OpenAI API. In these instances, a prompt must be designed according to a given data feed while maintaining the generality and coverage for various input data features. In this study, we found that providing clear guidance on the output format, emphasizing evidence-based inference, providing chain of thought prompting by asking for tumor size information, and providing specific examples are critical in improving the efficiency and accuracy of extracting structured data from the free-text pathology reports. The approach employed in this study effectively leverages the OpenAI API for batch queries of ChatGPT services across a large set of tasks with similar input data structures, including but not limited to pathology reports and EHR.

Our evaluation results show that the ChatGPT (gpt-3.5-turbo-16k) achieved an overall average accuracy of 89% in extracting and estimating lung cancer staging information and histology subtypes compared to pathologist-curated data. This performance is very promising because some scanned pathology reports included in this study contained random characters, missing parts, typos, varied formats, and divergent information sections. ChatGPT also outperformed traditional NLP methods. Our case analysis shows that most misclassifications were due to a lack of knowledge of detailed pathology terminology or very specialized information in the current versions of ChatGPT models, which could be avoided with future model training or fine-tuning with more domain-specific knowledge.

While our experiments reveal ChatGPT’s strengths, they also underscore its limitations and potential risks, the most significant being the occasional “hallucination” phenomenon 27 , 28 , where the generated content is not faithful to the provided source content. For example, the responses to blank or near-blank reports reflect this issue, though these instances can be detected and corrected due to convergence towards an “attractor”.

The phenomenon of ‘hallucination’ in LLMs presents a significant challenge in the field. It is important to consider several key factors to effectively address the challenges and risks associated with ChatGPT’s application in medicine. Since the output of an LLM depends on both the model and the prompt, mitigating hallucination can be achieved through improvements in GPT models and prompting strategies. From a model perspective, model architecture, robust training, and fine-tuning on a diverse and comprehensive medical dataset, emphasizing accurate labeling and classification, can reduce misclassifications. Additionally, enhancing LLMs’ comprehension of medical terminology and guidelines by incorporating feedback from healthcare professionals during training and through Reinforcement Learning from Human Feedback (RLHF) can further diminish hallucinations. Regarding prompt engineering strategies, a crucial method is to prompt the GPT model with a ‘chain of thought’ and request an explanation with the evidence used in the reasoning. Further improvements could include explicitly requesting evidence from input data (e.g., the pathology report) and inference rules (e.g., AJCC rules). Prompting GPT models to respond with ‘Unknown’ when information is insufficient for making assertions, providing relevant context in the prompt, or using ‘embedding’ of relevant text to narrow down the semantic subspace can also be effective. Harnessing hallucination is an ongoing challenge in AI research, with various methods being explored 5 , 27 . For example, a recent study proposed “SelfCheckGPT” approach to fact-check black-box models 29 . Developing real-time error detection mechanisms is crucial for enhancing the reliability and trustworthiness of AI models. More research is needed to evaluate the extent, impacts, and potential solutions of using LLMs in clinical research and care.

When considering using ChatGPT and similar LLMs in healthcare, it’s important to thoughtfully consider the privacy implications. The sensitivity of medical data, governed by rigorous regulations like HIPAA, naturally raises concerns when integrating technologies like LLMs. Although it is a less concern to analyze public available de-identified data, like the lung cancer pathology notes used in this study, careful considerations are needed for secured healthcare data. More secured OpenAI services are offered by OpenAI security portal, claimed to be compliant to multiple regulation standards, and Microsoft Azure OpenAI, claimed could be used in a HIPAA-compliant manner. For example, de-identified Osteosarcoma pathology notes were analyzed by Microsoft Azure OpenAI covered by the Business Associate Agreement in this study. In addition, exploring options such as private versions of these APIs, or even developing LLMs within a secure healthcare IT environment, might offer good alternatives. Moreover, implementing strong data anonymization protocols and conducting regular security checks could further protect patient information. As we navigate these advancements, it’s crucial to continuously reassess and adapt appropriate privacy strategies, ensuring that the integration of AI into healthcare is both beneficial and responsible.

Despite these challenges, this study demonstrates our effective methodology in “prompt engineering”. It presents a general framework for using ChatGPT’s API in batch queries to process large volumes of pathology reports for structured information extraction and estimation. The application of ChatGPT in interpreting clinical notes holds substantial promise in transforming how healthcare professionals and patients utilize these crucial documents. By generating concise, accurate, and comprehensible summaries, ChatGPT could significantly enhance the effectiveness and efficiency of extracting structured information from unstructured clinical texts, ultimately leading to more efficient clinical research and improved patient care.

In conclusion, ChatGPT and other LLMs are powerful tools, not just for pathology report processing but also for the broader digital transformation of healthcare documents. These models can catalyze the utilization of the rich historical archives of medical practice, thereby creating robust resources for future research.

Data processing, workflow, and prompt engineering

The lung cancer data we used for this study are publicly accessible via CDSA ( https://cancer.digitalslidearchive.org/ ) and TCGA ( https://cBioPortal.org ), and they are de-identified data. The institutional review board at the University of Texas Southwestern Medical Center has approved this study where patient consent was waived for using retrospective, de-identified electronic health record data.

We aimed to leverage ChatGPT to extract and estimate structured data from these notes. Figure 7a displays our process. First, scanned pathology reports in PDF format were downloaded from TCGA and CDSA databases. Second, R package pdftools, an optical character recognition tool, was employed to convert scanned PDF files into text format. After this conversion, we identified reports with near-empty content, poor scanning quality, or missing report forms, and those cases were excluded from the study. Third, the OpenAI API was used to analyze the text data and extract structured data elements based on specific prompts. In addition, we extracted case identifiers and metadata items from the TCGA metadata file, which was used to evaluate the model performance.

a Illustration of the use of OpenAI API for batch queries of ChatGPT service, applied to a substantial volume of clinical notes — pathology reports in our study. b A general framework for integrating ChatGPT into real-world applications.

In this study, we implemented a problem-solving framework rooted in data science workflow and systems engineering principles, as depicted in Fig. 7b . An important step is the spiral approach 30 to ‘prompt engineering’, which involves experimenting with subtasks, different phrasings, contexts, format specifications, and example outputs to improve the quality and relevance of the model’s responses. It was an iterative process to achieve the desired results. For the prompt engineering, we first define the objective: to extract information on TNM staging and histology type as structured attributes from the unstructured pathology reports. Second, we assigned specific tasks to ChatGPT, including estimating the targeted attributes, evaluating certainty levels, identifying key evidence of each attribute estimation, and generating a summary as output. The output was compiled into a JSON file. In this process, clinicians were actively formulating questions and evaluating the results.

Our study used the “gpt-3.5-turbo” model, accessible via the OpenAI API. The model incorporates 175 billion parameters and was trained on various public and authorized documents, demonstrating specific Artificial General Intelligence (AGI) capabilities 5 . Each of our queries sent to ChatGPT service is a “text completion” 31 , which can be implemented as a single round chat completion. All LLMs have limited context windows, constraining the input length of a query. Therefore, lengthy pathology reports combined with the prompt and ChatGPT’s response might exceed this limit. We used OpenAI’s “tiktoken” Python library to estimate the token count to ensure compliance. This constraint has been largely relaxed by the newly released GPT models with much larger context windows. We illustrate the pseudocode for batch ChatGPT queries on a large pathology report set in Supplementary Fig. 5 .

Model evaluation

We evaluated the performance of ChatGPT by comparing its output with expert-curated data elements provided in the TCGA structured data using the testing data set. Some staging records in the TCGA structured data needed to be updated; our physicians curated and updated those records. To mimic a real-world setting, we processed all reports regardless of data quality to collect model responses. For performance evaluation, we only used valid reports providing meaningful text and excluded the reports with near-empty content, poor scanning quality, and missing report forms, which were reported as irregular cases. We assessed the classification accuracy, F1, Kappa, recall, and precision for each attribute of interest, including pT, pN, overall stage, and histology types, and presented results as accuracy and confusion matrices. Missing data were excluded from the accuracy evaluation, and the coverage rate was reported for predicted values as ‘unknown’ or empty output.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The lung cancer dataset we used for this study is “Pan-Lung Cancer (TCGA, Nat Genet2016)”, ( https://www.cbioportal.org/study/summary?id=nsclc_tcga_broad_2016 ) and the “luad” and “lusc” subsets from CDSA ( https://cancer.digitalslidearchive.org/ ). We have provided a reference regarding how to access the data 32 . We utilized the provided APIs to retrieve clinical information and pathology reports for the LUAD (lung adenocarcinoma) and LUSC (lung squamous cell carcinoma) cohorts. The pediatric data are the EHR data from UTSW clinic services. The data is available from the corresponding author upon reasonable request and IRB approval.

Code availability

All codes used in this paper were developed using APIs from OpenAI. The prompt for the API is available in Fig. 2 . Method-specific code is available from the corresponding author upon request.

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Acknowledgements

This work was partially supported by the National Institutes of Health [P50CA70907, R35GM136375, R01GM140012, R01GM141519, R01DE030656, U01CA249245, and U01AI169298], and the Cancer Prevention and Research Institute of Texas [RP230330 and RP180805].

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Quantitative Biomedical Research Center, Peter O’Donnell School of Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA

Jingwei Huang, Donghan M. Yang, Ruichen Rong, Kuroush Nezafati, Colin Treager, Shidan Wang, Xian Cheng, Yujia Guo, Guanghua Xiao, Xiaowei Zhan & Yang Xie

Department of Pathology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA

Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA

Laura J. Klesse

Department of Internal Medicine, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, USA 75390, USA

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Contributions

J.H., Y.X., X.Z. and G.X. designed the study. X.Z., K.N., C.T. and J.H. prepared, labeled, and curated lung cancer datasets. D.M.Y., X.C., Y.G., L.J.K. prepared, labeled, and curated osteosarcoma datasets. Z.C. provided critical inputs as pathologists. Y.X., G.X., E.P. provided critical inputs for the study. J.H. implemented experiments with ChatGPT. R.R. and K.N. implemented experiments with N.L.P. J.H., Y.X., G.X. and S.W. conducted data analysis. Y.X., G.X., J.H., X.Z., D.M.Y. and R.R. wrote the manuscript. All co-authors read and commented on the manuscript.

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Correspondence to Xiaowei Zhan or Yang Xie .

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Huang, J., Yang, D.M., Rong, R. et al. A critical assessment of using ChatGPT for extracting structured data from clinical notes. npj Digit. Med. 7 , 106 (2024). https://doi.org/10.1038/s41746-024-01079-8

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Received : 24 July 2023

Accepted : 14 March 2024

Published : 01 May 2024

DOI : https://doi.org/10.1038/s41746-024-01079-8

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