research paper related to medical technology

• Research article
• Open access
• Published: 26 February 2018

The use of advanced medical technologies at home: a systematic review of the literature

• Ingrid ten Haken 1 ,
• Somaya Ben Allouch 1 &
• Wim H. van Harten 2 , 3  

BMC Public Health volume  18 , Article number:  284 ( 2018 ) Cite this article

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The number of medical technologies used in home settings has increased substantially over the last 10–15 years. In order to manage their use and to guarantee quality and safety, data on usage trends and practical experiences are important. This paper presents a literature review on types, trends and experiences with the use of advanced medical technologies at home.

The study focused on advanced medical technologies that are part of the technical nursing process and ‘hands on’ processes by nurses, excluding information technology such as domotica. The systematic review of literature was performed by searching the databases MEDLINE, Scopus and Cinahl. We included papers from 2000 to 2015 and selected articles containing empirical material.

The review identified 87 relevant articles, 62% was published in the period 2011–2015. Of the included studies, 45% considered devices for respiratory support, 39% devices for dialysis and 29% devices for oxygen therapy. Most research has been conducted on the topic ‘user experiences’ (36%), mainly regarding patients or informal caregivers. Results show that nurses have a key role in supporting patients and family caregivers in the process of homecare with advanced medical technologies and in providing information for, and as a member of multi-disciplinary teams. However, relatively low numbers of articles were found studying nurses perspective.

Conclusions

Research on medical technologies used at home has increased considerably until 2015. Much is already known on topics, such as user experiences; safety, risks, incidents and complications; and design and technological development. We also identified a lack of research exploring the views of nurses with regard to medical technologies for homecare, such as user experiences of nurses with different technologies, training, instruction and education of nurses and human factors by nurses in risk management and patient safety.

Peer Review reports

As a result of demographic changes and the rapidly increasing number of older patients, there is a need for cost savings and health reforms, which include an increased move from inpatient to outpatient care in most industrialized countries over the last 10–15 years [ 1 , 2 ]. As a consequence, the transfer of advanced medical devices into home settings was considerable and it is expected that there will be a further increase in the near future [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ].

When ‘an increase’ in the number of medical technologies used at home is mentioned, it is not clear which and how many technologies are involved. Today, there are an estimated 500,000 different kinds and types of medical devices available on the world market [ 8 , 9 ]. The European Commission (EC) publishes data regarding legislation and regulations for medical devices, but the actual figures for medical technologies in outpatient practice are not available [ 10 ]. The U.S. National Center for Health Statistics (NCHS) stated that technologies have shifted from hospitals into the home, but it too does not illustrate its findings with statistics [ 11 ]. We searched for data with regard to the actual number of medical technologies used in home settings and it proved difficult to find any systematic data sets available throughout the international landscape.

An important condition for the application of medical technology in the home setting is that quality of care and patient safety must be guaranteed [ 6 ]. From a historical perspective medical technologies were designed for hospital settings [ 12 , 13 ]. This means that specific factors regarding the implementation and use at home now need to be taken into account [ 7 , 14 , 15 ]. In general, risks with medical technologies can be classified regarding (a) environmental factors; (b) human factors and (c) technological factors [ 16 ]. Human factors, however, are very important in patient safety in both hospital and in home settings [ 1 , 6 , 12 ]. For example, a major risk factor is the number of users and handovers in the chain of care. In home settings, a sometimes impressive number of different users of medical technology, often with various levels of training, instruction or education, are involved. Although patient empowerment moves control to the patient and/or relatives, an important user group is that of professional nurses. Understanding user experiences and information about adverse events and near incidents are important aspects for developing knowledge regarding implementation and use in home care setting. Sharing this knowledge can support patients and caregivers, and especially nurses in their professional work and will also contribute to patient safety and quality of care.

Therefore, there is a need to address the question first, which types of technologies are used at home; second, how frequently are they used and third, what trends can be distinguished. Additional research questions are whether there are any scientific data regarding particular user experiences; training, instruction and education; safety and risks, and finally, what can be concluded about the role of nurses in using medical technologies in the home environment. The objective of this paper therefore is to present a systematic literature search on the international state of art concerning various aspects of the use of advanced medical technologies at home.

Definitions

First, we want to clarify some definitions. In general, ‘health technology’ refers to the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem and improve quality of life [ 17 ]. The World Health Organization [ 8 ] uses the definition of ‘medical device’ as ‘An article, instrument, apparatus or machine that is used in the prevention, diagnosis or treatment of illness or disease, or for detecting, measuring, restoring, correcting or modifying the structure or function of the body for some health purpose …….’. A specification for a home use medical device is: ‘A medical device intended for users in any environment outside of a professional healthcare facility. This includes devices intended for use in both professional healthcare facilities and homes’ [ 18 ].

The landscape of medical devices is diverse with technologies varying from relatively simple to very complex devices. Wagner et al. [ 19 ] stated that ‘high-tech dependency’ (for children) matches with ‘technology-dependency’ if it concerns ‘a medical device to compensate for the loss of a vital bodily function and substantial and ongoing nursing care to avert death or further disability’. ‘The needs of these patients may vary from the continuous assistance of a device and highly trained caretaker to less frequent treatment and intermittent nursing care’ [ 20 ]. Although patients dependent of advanced medical technologies at home are often medically stable, they sometimes have high technical needs and may be expected to need long-term recovery. They also require skilled nursing [ 21 ] and a considerable degree of advanced decision making, planning, training and oversight [ 22 ]. An overall definition of ‘advanced medical technology’ is: ‘Medical devices and software systems that are complex, provide critical patient data, or that directly implement pharmacologic or life-support processes whereby inadvertent misuse or use error could present a known probability of patient harm’ [ 23 ]. Examples of advanced medical technologies used at home include ventilators for respiratory support, systems for haemo- or peritoneal dialysis and infusion pumps to provide nutrition or medication.

In the Netherlands, the National Institute for Public Health and the Environment (RIVM) [ 24 ] uses the following definition:

Advanced medical technology or high-tech technology in the home setting is defined as technology that is part of the technical skills in nursing and meets the following conditions:

technology that is advanced or high-tech, for example equipment with a plug, an on/off switch, an alarm button and a pause button;

technology that had been applied formerly only in hospital care, but that is now also often applied in home settings;

technology that can be categorized as ‘supporting physiological functions’, ‘administration’ or ‘monitoring’.

Within the Dutch classification of advanced medical technologies 19 different devices are identified (see Table  1 ), which will be used in this review as a basis to categorize the technologies. It is a classification format in which specific advanced technologies are defined. Terms as ‘advanced medical technology’ (from now on abbreviated as AMT) will be used consistently as synonyms for ‘complex medical technology’ and ‘high-tech medical technology’. The term ‘technology’ will be used in the meaning of ‘device’ or ‘equipment’. The target is on technologies that are instrumental and ‘hands on’ use by nurses in the care for patients. This means that information technology (IT) based technologies as domotica (automation for a home) are not part of the study.

Eligibility and search strategy

The systematic review of the literature was conducted early 2016. Key concepts for the review were ‘medical technologies’ or ‘medical devices’, and ‘home settings’. The concept of ‘home settings’ is related to the terms ‘home nursing’ and ‘home care service’, of which the stem is ‘home’. Combining the key concepts provided the search string: (‘medical technology’ OR ‘medical device’). As domotica is not part of the study, the search string was extended with: AND NOT (eHealth OR telecare OR telemedicine). The exact search string is (“medical technology” OR “medical devices”) AND home AND NOT (ehealth OR telecare OR telemedicine). Online databases MEDLINE, Scopus and Cinahl were searched electronically using the search string to obtain data.

Inclusion and exclusion criteria

Criteria for selection were defined prior to the search process. General criteria for inclusion were:

Year of publication: 2000–2015.

An abstract or an article (with or without abstract) has to be available, containing reference to AMT information.

The article is published in English, German, French or Dutch/Flemish language.

If medical technology is cited, it has to conform to the definition of ‘advanced medical technology’ [ 24 ].

The abstract or the article has to contain empirical material. For the purpose of this review, ‘empirical material’ has been defined as: AMT which is designed for the home setting, or where the design or choices took into account the setting of the home, or where the medical technology has been tested for the home or if the medical technology is already on the market and being used in the home setting.

For further selection, inclusion criteria related to the key concepts for title and abstract were applied, such as ‘advanced medical technology’, ‘high-tech medical technology’, ‘home-centred health-enabling technology’ and ‘care at home’. The classification of the RIVM (see Table 1 ) has been taken as a basis to categorize technologies in this review. Domotica and telemonitoring technologies scored under ‘monitoring’, such as fetal cardiotocography, and respiratory and circulatory monitoring, were left out. If the abstract or article was about electronic health records, ‘smart home’, ambient intelligence, pervasive computing, software of devices, smartphone or surgical robots, the article was also removed from selection. Technologies as ‘VAD (ventricular assist device)’, ‘dental devices’ and ‘AED (automatic external defibrillator)’ were not seen as part of the technical nursing process and these records were left out as well. Studies conducted in the hospital, hospice or nursing home settings were also excluded. An overview of all inclusion and exclusion criteria can be found in Table  2 .

Screening process

The search in the online databases using the search string, identified a total of 1287 references. After checking for duplicates, 1070 articles remained. Those articles were reviewed by a reviewer for titles and abstracts on basis of the inclusion and exclusion criteria. A double check was performed by two reviewers, who independently screened random samples of 20% of the articles. There was an initial agreement of 88%. In case of disagreement about the inclusion of an article, the decision was based on a joint discussion by all three reviewers to an agreement of 100% and the resulting screening policy was applied to the rest of the abstracts. Based on the selected titles and/or abstracts, articles were retrieved or requested in full text and assessed for eligibility. Some articles were excluded from further study, for reasons of ‘full text not available’ or the article contained no empirical material. Finally, 87 studies remained which were included in the analysis (see Table  3 ). A graphical representation of the screening process has been included in Fig.  1 .

PRISMA flowchart

Appraisal of selected studies

To conduct the systematic literature search on the international state of art concerning various aspects of the use of advanced medical technologies at home, several sources are consulted. To guarantee a scientific standard, only articles were retrieved from academic databases. MEDLINE refers to journals for biomedical literature from around the world; Cinahl contains an index of nursing and research journals covering nursing, biomedicine, health sciences librarianship, alternative medicine, allied health and more. These databases related to discipline have been supplemented with Scopus, which is considered to be the largest abstract and citation database of peer-reviewed literature. Grey literature, such as national and international reports on regulations and safety of medical technologies, is also used to illustrate the background of the problem statement and describe definitions. The Classification of advanced medical technologies in the Netherlands according to the National Institute for Public Health and the Environment (RIVM) has been used as a framework to categorise the medical technologies in the selected articles. No methodological conditions of selected studies were applied in advance and the quality criterion we applied was that of the article had to contain empirical material, as we wanted to obtain an comprehensive overview of published studies of any design and to get insight in a variety of contents.

Categorization of included articles

The characteristics of the included articles are outlined in Table  3 . All included articles were categorized by year of publication and the type of research, like the designs, methods and used instruments in the studies. Research features were synthesized where possible into overarching categories. For example, ‘systematic review’ and ‘narrative review’ were scored as ‘review’ and instruments as ‘semi-structured interview’ and ‘in-depth individual interview’ were both assigned to the category ‘interview’.

For each study, the medical technology or technologies on which the study was based was scored. The categorization was in accordance with the classification of AMTs (see Table 1 ). For example, the devices ‘continuous positive airway pressure (CPAP)’ and ‘negative pressure ventilation (NPV) have both been categorized as ‘respiratory support’; and the devices ‘jejeunostomy tube’ and ‘gastronomy tube’ as ‘enteral nutrition’. With regard to the category ‘dialysis’, further subdivision was made by using ‘haemo dialysis’ and ‘peritoneal dialysis’. If in an article a medical technology was mentioned as an example, but was no subject of study, then the technology was not scored.

‘Medical diagnosis (or diagnoses)’ as mentioned in the studies, was included in the analysis only if it was related to the medical technology as the subject of study, not if it has been mentioned as an example. In some cases, an underlying cause of diagnosis was indicated. For example, ‘chronic respiratory failure due to congenital myopathy’, in itself a neurological disorder, has been scored as ‘neurological disorder’. Diseases or disorders have been classified as much as possible under the overarching name. For example ‘pneumonia’ and ‘cystic fibrosis’ are categorized under ‘respiratory failure’, and ‘gastroparesis’ and ‘Crohns disease’ under ‘gastrointestinal disorder’. The category ‘other’ contains diagnoses which occur only once, such as ‘chromosomal anomaly’, or which are not yet determined, like ‘chronic diseases’ or ‘congenital abnormalities’.

In relation to the research questions, articles were classified regarding one of the following categories and, where appropriate, into subcategories:

User experiences

Training, instruction and education, safety, risks, incidents and complications.

From an analysis of the articles, additional categories of content emerged:

Design and technological development

Application with regard to certain diseases or disorders, indication for and extent of use

Policy and management

Types of medical technologies used, frequency of use and trends.

In four of the 87 articles (5%) there were no specific medical technologies mentioned as a subject of study (see Table  4 ). Almost half of the studies (45%) considered medical technologies for respiratory support and 39% devices for dialysis, either haemo- ( n  = 18), peritoneal- ( n  = 15) or dialysis not specified ( n  = 1). Of the studies, 29% reported on devices for oxygen therapy. In addition, there has been relatively more research conducted on equipment for ‘infusion therapy’ ( n  = 19; 22%), parenteral nutrition and enteral nutrition with a score of 20% each ( n  = 17). Relatively little research has been carried out on suction devices (8%), external electrostimulation (5%), nebulizer (5%), insulin pump therapy (3%), sleep apnea treatment (2%), patient lifting hoists (2%), vacuum assisted wound closure (1%) and continuous passive motion (1%). None of de studies considered medical technologies with regard to decubitus treatment, skeletal traction or UV (ultraviolet) therapy.

Table 4 shows that on the years 2000 and 2001 no relevant articles on the subject were found. Over the period 2000–2005, 17 articles were published, the same number over 2006–2010, and there has been a substantial increase in the number of publications to 54 over the years 2011–2015. In general, it can be concluded that more frequent investigated technologies show a fairly even distribution of publications over the years 2000–2015. Technologies, on which little research had been done, except for nebulizers, have been mainly investigated since 2010. An increase of published articles over the years 2000–2015 is apparent particularly for haemo dialysis and to a lesser extent, for devices for enteral- and parenteral nutrition. As mentioned before, several studies reported on the increase of the number of medical technologies used in home settings, but concrete data are not available. However, the number of studies and the visible trends may be indicative of the frequency of use.

In 63% of the cases ( n  = 55), a medical diagnosis (or diagnoses) was mentioned in the article. Where a diagnosis has been mentioned, in almost half of the studies ( n  = 26; 47%) it concerned diagnoses in the field of respiratory failure (see Fig.  2 ). This is not surprising, since ‘respiratory support’ is the medical technology most commonly found in the articles, similarly ‘oxygen therapy’ has also been considered relatively often. Diagnoses with regard to neurological disorders occurred in 42% of the studies ( n  = 23). Just over a quarter of the studies (27%) considered diagnoses ‘other’, such as ‘sepsis’, ‘chromosomal anomaly’ or other not specified medical disorders, nearly a quarter (24%) considered ‘cancer’ and 22% kidney disorders ( n =  12).

Number of medical diagnoses mentioned in articles on AMTs ( n  = 87, multiple answers possible)

An analysis of the used research designs identified that 64% ( n  = 56) of the studies used an observational (non-experimental) design and only a small part of the studies ( n  = 5; 6%) used an experimental design, such as a Randomized Control Trial (RCT). Of the included studies 19 were reviews and 8 were essays. A quantitative design ( n  = 37) was used more frequently than a qualitative design ( n  = 25); and only one study applied ‘mixed methods’ (quantitative and qualitative). Just over one-third of the studies (35%) used a descriptive design, and a similar number used a cross-sectional study (36%). Case series were used in 12% of the articles and a cohort-study in 9%. A phenomenological approach was applied in 16% of the records. Research instruments most frequently used were interviews (33%) and survey/questionnaires (21%). In 10% of the cases other instruments were used, including different types of assessments or tests.

With regard to the categories of content, most research has been carried out on ‘user experiences’ (see Fig.  3 ): just over one-third of the articles ( n  = 31; 36%) focused on this topic. Of these articles almost all studies focused on experiences of patients or informal caregivers ( n  = 29) and only a small number ( n  = 2) considered the user experiences of nurses or other professionals (see Table  5 ). More than half of the studies ( n  = 19) used a qualitative research design; of these 13 used a phenomenological approach. The goal of these studies was to elicit the essence of human phenomena as experienced by the users. Seven studies used a quantitative design and one an integrated mixed method. Three of the studies applied a grounded theory approach and two an experimental design (randomized controlled trial). The research instruments in this content category to collect data were interviews, either semi-structured or in-depth, and a survey. About two-thirds of the articles regarding ‘user experiences’ were published in the period 2011–2015, with an accent on the psychosocial impact of patients or informal caregivers.

Number of articles on AMTs with main content categories ( n  = 87)

Relatively little research was found on ‘training, instruction, education’ ( n  = 7), for the use of AMTs in home settings. It was remarkable that all the studies identified as focusing on this topic, concentrated on one category of AMT. Respiratory support was the subject of study in four instances and in the other three, the focus was on technologies for enteral nutrition, haemo dialysis and external electro-stimulation. Four of the seven articles utilized quantitative methods, among which three of them used an observational non-experimental design and one was an experimental randomized double-blind clinical trial. Another study within the initial seven articles used a qualitative observational non-experimental design, one was a review and another was in essay format.

In total, 22% of the articles discussed topics on safety, risks, incidents and complications ( n  = 19). In the majority of cases ( n  = 13) general aspects about the subject, for instance safe use, factors affecting safety, a safe transfer of the equipment and monitoring of assessing safety were considered. One article described technological factors with regard to safety, three articles reported on environmental factors and two explored human factors. Safety aspects were explored over a wide range of medical technologies. Five articles were reviews and one an essay. Quantitative methods were used in ten of the cases, particularly for monitoring, evaluating and assessing safety, technological and environmental factors. Only three studies used a qualitative design. Retrospective chart reviews or case series were used to collect data in some cases of unforeseen events. Table 5 shows about a doubling of published articles in the period 2011–2015 regarding this content category, compared to the previous period 2000–2010.

Approximately 20% of the selected articles considered the content category ‘design and technological development of the medical device’ ( n  = 17). The studies each focused on only one type of AMT and treated a relative wide range of eight different categories, such as ‘respiratory support’, ‘oxygen therapy’, ‘haemo dialysis’, ‘infusion therapy’, ‘insulin pump therapy’ and ‘enteral nutrition’, but also ‘external electrostimulation’ and ‘patient lifting hoists’. Interestingly, in this group of articles, relatively often ( n  = 6) no medical diagnosis was mentioned. Around half of the studies ( n  = 8) referring to this topic were in review or essay format. All other studies used a quantitative research design and throughout the search no application of qualitative designs were found. Two studies used an experimental study design (randomized crossover trial) to obtain data and two described a prospective cohort study. The majority of papers ( n  = 11) were published in the period 2011–2015 and six in the preceding period up to and including 2010.

Seven articles concerned the application of AMTs, all of them devices with regard to at least respiratory support and/or nutritional support. Five studies used a non-experimental quantitative design including the analysis of clinical data, such as record reviews or cohort studies, and two articles were reviews. Most articles on this subject ( n  = 5) were published in the period 2012–2015.

Six articles described policy or management systems in different countries regarding the use of AMTs at home. The majority of the articles ( n = 4 ) were in essay or review format. The other papers concerned a qualitative cross-sectional case study analysis and an observational quantitative study in which data are collected prospectively using a database. The categories of content will now be discussed in greater detail.

Content description and trends to secondary research questions

In this category, 22 articles described the psychosocial impact on patients or informal caregivers from the use of medical technologies at home. Living at home with the assistance of medical technology needs a range of adjustments. Fex et al. [ 25 , 26 ] state that self-care is more than mastering the technology, in terms of the health-illness transition, it requires ‘…. an active learning process of accepting, managing, adjusting and improving technology’. When it comes to children, they have to learn to incorporate disability, illness and technology actively within their process of growing up [ 27 ]. It seems that the use of medical technologies in the home can have both a positive and a negative psychosocial impact on patients and their families, which in turn causes ambivalence in experiences [ 27 , 28 ]. On the one hand, patients in general gain more independence, an enhanced overall health and a better quality of life [ 29 , 30 , 31 , 32 , 33 , 34 ]. On the other hand, for some patients the experience is one of dependency on others for executing daily activities, and these circumstances, to some extent, a social restricted live and perceived stigmatization [ 29 , 30 ]. The situation in which patients need to use medical technology at home also affects family functioning and requires next of kin responsibilities [ 35 , 36 , 37 ]. As a result, next of kin caregivers are frequently faced with poor sleep quality and quantity, and/−or other significant psychosocial effects [ 38 , 39 , 40 , 41 ]. Nevertheless, family members had a positive attitude to the concept of bringing the technology into the home [ 42 ]. Knowledge of how to use the technology and permanent access to support from healthcare professionals and significant others, enabled next of kin caregivers to take responsibility for providing necessary care and to facilitate patients learning to provide self-care [ 25 , 36 , 42 , 43 , 44 ]. Bezruczko et al. [ 45 , 46 ] developed a measure of mothers’ confidence to care for children assisted with medical technologies in their homes. To provide high quality sustainable care, nurses have to recognize and understand the psychosocial dimensions for both patients and family members which arise as a result of changing role and providing care for the patients. The need to provide emotional support and support with appropriate coping strategies is a key professional role [ 25 , 26 , 47 ]. Insight into the psychosocial effects on those involved can be used to assist designers of medical devices to find strategies to better facilitate the integration of these technologies into the home [ 28 ].

Seven articles reported on the usability, barriers and accessibility experienced by patients or informal caregivers. Findings in these studies showed that several technologies were rarely perceived as user-friendly and that home medical devices inadequately met the needs of individuals with physical or sensory deficits [ 48 , 49 ]. An accessible design which meets the diversity of individual user needs, characteristics and features would be better able to help patients manage their own treatment and so could contribute to the quality of care and safety of patients and lay users [ 50 , 51 ]. Munck et al. [ 52 ] stated that restricted patients were reminded daily of the medical technology and were more dependent on assistance from healthcare professionals than masterful patients.

In contrast to the group of patients or informal caregivers, only two papers in this content category focused on the user experiences of nurses or other professional caregivers. The review demonstrates that to maintain patient safety, more education on application of medical devices for users is needed together with improved awareness and understanding of how to use the medical technology correctly in a patient-safe way [ 53 , 54 ]. More collaboration between all involved ‘actors’ in the process of care is also requisite. Continuity among carers, trust between patient and carers and supportive communication between informal and professional caregivers are important factors for the successful implementation of medical technologies in the home environment while maintaining patient safety [ 44 , 51 , 53 , 54 , 55 ].

Three articles regarding this topic focused on nurses or other professionals and four on the patients or informal caregivers. The results showed that successful use of advanced medical technologies at home requires adequate staff education and training programmes. Although many topics in educational programmes are suitable for different types of professionals in care provision, the focus for the level and application of information can vary for Registered Nurses and unregistered care staff. In addition, for overall learning experiences to be of maximum benefit there is a need for a clear focus on the specific client groups [ 56 ]. According to Sunwoo et al. [ 57 ], in the case of home non-invasive ventilation the degree of clinical support needed is extremely variable given the mixed indications for this respiratory support. A relatively simple procedure, such as the replacement of a feeding tube, can be performed by nurses, the patient and informal caregivers, provided they are trained well [ 58 ]. However, several studies revealed the complexity of the education needed by patients and informal caregivers for the use of advanced medical technologies at home [ 59 , 60 ]. Nevertheless, the studies revealed that a structured education programme, specific training, or the support of a dedicated discharge coordinator has several advantages [ 59 , 61 , 62 ]. It was evident that good preparation by patients or informal caregivers may result in a shorter length of stay in hospital, a better performance with regard to the use of the equipment or less requests by patients and/or families for assistance.

Most articles regarding this topic ( n  = 13) reported on safety in general, like aspects of safe use, factors affecting safety, complications and prevention of incidents in the home. Some identified the risk factors and the complications that may arise [ 63 , 64 , 65 ], where Stieglitz et al. [ 66 ] also emphasize that human error is the main reason for critical incidents and that regular instruction for medical staff and patients is necessary. To prevent untoward and adverse events, evidence based guidelines, recommendations on the preferred methods for managing the equipment, troubleshooting techniques for potential complications and monitoring activities are necessary [ 67 , 68 ]. Faratro et al. [ 68 ] added that key performance and quality indicators are important mechanisms to ensure patient safety when using a medical device in the home. Methods to address or evaluate patient safety issues are for example, a home visit audit tool, a nationwide adverse event reporting system, programs such as the Medical Product Safety Network HomeNet, or, in the case of peripherally inserted central catheters (PICCs) a central catheter stabilization system [ 69 , 70 , 71 , 72 ]. However, a study conducted by Pourrat and Neuville [ 73 ] in France found that there are very few internal medical devices vigilance reports found within organizations that deliver devices for home parenteral nutrition and that safety management could be improved. The safe transfer of medical devices from a hospital setting to the home and vice versa, comes with several challenges regarding technological, environmental and human factors [ 14 ]. While many hospitals have developed policies to control the pathways of home-used devices in the hospitals, in case patients take them into the hospital when they are admitted for treatment [ 74 ]. Improvement of the safety of devices intended for use in home settings, implies also improvement of safety when their transfer to the hospital settings is urgently needed.

One article considered the technological factors, three the environmental and two the human factors. An example of research on the technological factors of safety related aspects of medical technologies used in home settings by Hilbers et al. [ 75 ] found that manufacturers pay insufficient attention to safety-related items in technical documentation for the use in the home setting. For instance, the environmental factor of electricity blackout leads to electrically powered medical devices failing. Studies show that this type of event causes a dramatic increase in appeal for access to emergency or hospital facilities, and that disaster preparation needs to include the specific needs of patients reliant on electrically driven devices [ 76 , 77 , 78 ]. Regarding human factors impacting on safety aspects, one article assessed the suitability of a particular theoretical framework for understanding safety-critical interactions of patients using medical devices in the home [ 79 ], while Tennankore et al. [ 80 ] described adverse events in home haemodialysis by the use of patients. It was remarkable that none of the articles focused on human factors with regard to the use of medical technologies at home by nurses or other professional caregivers.

Of those articles that focused on this topic, ten reported on the comparison between different types of medical technologies, or their advantages and disadvantages. The comparison of different devices for oxygen therapy was made by two articles [ 81 , 82 ] and one reported on the comparison of two types of enteral nutrition tubes [ 83 ]. Some studies regarding respiratory support considered the process of making a choice between different types of devices [ 84 , 85 , 86 ] while one paper considered the conditions for home-based haemo dialysis [ 87 ]. A minority, explored the individual characteristics and the clinical applications of several devices for respiratory support [ 88 , 89 ] and one considered devices for insulin pump therapy [ 90 ]. Seven papers discussed the technological development or effectiveness of medical technologies. The testing of devices for external electro-stimulation was described in two papers [ 91 , 92 ], with the testing of a new design patient lift was subject of one study [ 93 ]. Hanada and Kudou [ 94 ] explored the current status of electromagnetic interference with medical devices in the home setting, an issue of importance as more devices are considered for home use. The technological development of respiratory support for home use was part of one study [ 95 ], as were the possibilities of solar-assisted home haemo dialysis [ 96 ]. While the study by Pourtier [ 97 ] describes the advantages of analgesia pumps that can be read remotely by nurses, but also emphasizes the central position of a professional nurse in the transfer of information within a multi-disciplinary team.

Application with regard to certain diseases or disorders, indications for and extent of use

All articles described several aspects that need to be considered for use, such as clinical characteristics of the patients, indications for the use in the home setting, the technical availability of devices, the extent of their use at home or eventual complications and morbidity. It was important to note that all but one article ( n  = 6) were about children or related to adults with what are usually regarded as paediatric diseases. Results show that the use of AMTs at home among children after hospital discharge is common (in 20%–60% of cases), or is standard for patients with some disorders [ 98 , 99 , 100 , 101 ]. The timely application of advanced home medical technology benefits patients and can help to reduce respiratory morbidity [ 102 ]. Nevertheless, the rate of death of patients with Möbius syndrome using the devices at home was high (30%) [ 98 ], as was that of patients with intestinal failure dependent on home parental nutrition therapy in Brazil (75% for 5 years) [ 103 ]. The average cumulative survival of children needing home ventilation was found to be between 75 and 90%, depending on the medical diagnosis [ 104 ].

Three of the papers were concerned with costs and/or reimbursement. The application of medical technologies in the home environment can be cost-effective when compared to institutionalized care [ 22 , 105 , 106 ]. Nevertheless, successful employment of medical technologies in the home necessitates medical guidelines for the indicators for use, careful identification of patients as well as careful planning and attention to details [ 105 , 106 , 107 ]. Two studies concerned the dilemma’s for implementation of the technologies in home healthcare and emphasized the importance of cooperation in the chain of key stakeholders to maximize efficiency of high-tech healthcare at home, one with regard to the purchasing policy of medical technologies [ 108 ] and one with regard to the interventions of local community service centres and hospitals supporting optimal use of these technologies in the home setting [ 5 ].

The use of medical technologies in the home setting has drawn increased attention in health care over the last 15 years, as the feasibility of this type of medical support has rapidly grown. This article systematically reviewed the international literature with regard to the state of the art on this subject, in order to provide a comprehensive overview.

Trend analysis over the period 2000–2015 shows that most research has been conducted about respiratory support, dialysis and oxygen therapy; relatively little about vacuum assisted wound closure and continuous passive motion, and no about decubitus treatment, skeletal traction and UV therapy. A substantial increase in publications was found in the period 2011–2015. Although the number of studies on technologies is indicative of the extent to which they are used in home settings, however, no firm conclusions can be drawn about this.

This review also identified that most research is conducted with regard to ‘user experiences’ of medical technologies in the home, ‘safety, risks, incidents and complications’, and ‘design and technological development of medical technologies’. There have been relatively few studies which have explored the topic of training, instruction and education. Content analysis showed that the use of AMTs in the home setting can have both a positive and a negative psychosocial impact on the patients and their families, and that it has become part of self-management and patient empowerment. Successful use of advanced equipment requires adequate education and training programmes for both patients, informal caregivers and nurses or other professionals. When trying to maximize or assure safety, technological, environmental and human factors have to be taken into account, and it is evident that human factors are the main reason for critical incidents. Studies on the design and technological development of medical technologies emphasize that research is necessary to improve its possibilities and effectiveness. The research found on the application of the technologies focused predominantly on children and the results indicate that the rate of the use of home medical devices among children after hospital discharge is common. Also that when compared to institutionalized care, the application of medical technologies in the home environment can be cost-effective. Much is known, but information on several key issues is limited or lacking.

An important finding was that in almost all the reviewed articles, the study subjects were patients or informal caregivers with very few studies focused on the role and activities of nurses or other professionals as users. This was unexpected as nurses are the main group of users of AMTs at home and they have to transfer knowledge and skills on how to use the devices to patients and other caregivers. Nurses also have a key role in setting up and maintaining collaboration between all actors involved in the process of care with regard to the use of home medical technologies and in giving support to patients and family members in this respect. There is need to initiate further in depth research on AMTs use at home focusing on the role of specifically nurses.

Another interesting result was that, despite the fact that most adverse events with AMTs at home are caused by human factors, hardly any studies conducted on this subject were found. None of the articles focused on related human factors regarding the use by nurses or other professional caregivers, although this is the main user group. Research on this area could contribute to improved patient safety and quality of care. The results also revealed the tension between the advantages and disadvantages of medical technologies as experienced by patients at home. Important aspects needed to promote the benefits include improving the user-friendliness of the devices and attuning their designs for the use in home settings. This emphasizes the importance of professionals (and patient groups) working together with the designers with regard to sharing knowledge and user experiences of the use of AMTs at home in order to improve quality of care and patient safety. This collaboration emerged as of key importance in the successful use of AMTs in the home as well.

Although all included articles were retrieved from academic databases and served our purpose, there was considerable heterogeneity of quality of the studies. Most of the studies have explicitly described their research design, albeit to a greater or lesser extent. On the other hand, there were a few studies that did not even mention their methodological approach, though it could be derived from the description. Most included reviews are of moderate quality. Although findings are almost always described clearly, the search strategy and selection criteria used are often lacking. The quantitative studies are generally well described in different methodological aspects, such as selection of respondents, research design, data collection methods and analyses. Studies of qualitative nature show more variation in the depth with which the design is described. However, almost all qualitative studies have described the research instruments very well, such as semi-structured interviews or questionnaires. Despite the varying quality of the studies, we believe that the whole of different methodological approaches and the relatively large number of included studies ( n  = 87) has yielded a fairly reliable overview on the international state of art concerning various aspects of the use of advanced medical technologies at home. For future research, we recommend to emphasize the development of a more detailed methodological design, zooming in on specific technologies, using large databases or conducting large surveys, and focusing on specific groups of respondents. Both in quantitative and in qualitative studies, a good definition of the research question(s), selection of respondents, development of instruments and analysis of findings, contributes to validity, consistency and neutrality.

Some limitations do have to be taken into account with this review. Although we used the RIVM-definition of ‘advanced medical technology’, not all devices are considered as ‘complex devices’ by nurses in practice. For example, the use of an anti-decubitus mattress in the context of ‘decubitus treatment’ and ‘patient lifting hoists’ are considered by nurses as being of less or lower complexity. However, overall the RIVM-classification was found to be a good starting point, and provided a practical and useful framework from which to work to gain an insight and overview of available medical technologies. Of some of the chosen technologies defined using the RIVM-classification of AMTs, questions do have to be asked as to whether they really are part of the technical skills in nursing process. For example, ‘external electrostimulation’ and ‘continuous passive motion’ are mainly applied by physiotherapists, although with appropriate training nurses can apply them. Then too, devices regarded as only ‘monitoring’ were excluded from the review.

This systematic review study was designed to fill a gap in the current research by investigating what is known about different aspects of medical technologies used in the home. From the results it is obvious that a wide and growing range of medical technologies are used at home. Different types of technologies have been subject of study, increasingly –also in scope- over the period 2011–2015.

Professional nurses have a central role in the process of homecare which has to be recognized when considering use of AMTs at home. Nurses have to support patients and family caregivers and in consequence have a key role in providing information for, and as a member of multi-disciplinary teams. Closer collaboration by all actors involved in the process of care and feedback of user experiences to the designers is essential for the provision of high quality of care and patient safety.

This review also identified a lack of research exploring the perspectives of nurses in the processes involved in introducing and maintaining technology in homecare. Most of the research has been conducted regarding the experiences of patient experience and how informal caregivers perceive their role in using medical technologies at home. The few studies that were found, demonstrate the need for more research focused on the experiences of nurses working with advanced technologies in the home. The same applies to research on training, instruction and education to use medical technologies, as in these areas too, there was limited available research so here again there is need for further research. Despite the fact that most adverse events with medical technologies in home settings are caused by human factors, our findings also identified a lack of research in this area for nurses.

This study demonstrates that, although there is increasing attention on and recognition of the need for the use of medical technologies in the environment of the home, the research has not kept pace with the advances in care. Subjects such as user experiences of nurses with different technologies, training, instruction and education of nurses and human factors by nurses in risk management and patient safety urgently need to be investigated by further research.

Abbreviations

Automatic external defibrillator

Advanced medical technology

Continuous positive airway pressure

European Commission

Information technology

National Center for Health Statistics

Negative pressure ventilation

Peripherally inserted central catheters

Randomized Control Trial

National Institute for Public Health and the Environment

Ultraviolet

Ventricular assist device

World Health Organization

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The authors thank Ronnie van de Riet, head of the Medical Technical Care Team of the hospital ZiekenhuisGroep Twente, for his time and commitment to this project.

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Ingrid ten Haken is researcher in the research group Technology, Health & Care at Saxion University of Applied Sciences, Enschede, The Netherlands. Somaya Ben Allouch is head of the research group. Wim van Harten is professor at the University of Twente, Faculty Behavioural, Management and Social Sciences, department Health Technology & Services Research and CEO of Rijnstate general hospital, Arnhem, The Netherlands.

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ten Haken, I., Ben Allouch, S. & van Harten, W.H. The use of advanced medical technologies at home: a systematic review of the literature. BMC Public Health 18 , 284 (2018). https://doi.org/10.1186/s12889-018-5123-4

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Artificial Intelligence: How is It Changing Medical Sciences and Its Future?

Kanadpriya basu.

From the Covisus Inc, Monrovia, CA, USA

Ritwik Sinha

1 Adobe Research, San Jose, CA, USA

2 Whistle Labs, San Francisco, CA, USA

Treena Basu

3 Department of Mathematics, Occidental College, Los Angeles, CA, USA

Associated Data

The first step towards building an artificially intelligent system (after problem selection and development of solutions strategy) is data collection. The creation of well performing models relies on the availability of large quantities of high quality data. The issue of data collection is shrouded in controversy due to patient privacy and due to recent incidents of data breaches by major corporations. Advances in technology have resulted in increased computational and analytic power as well as the ability to store vast amounts of data. Technology such as facial recognition and gene analysis provides a path for an individual to be identified from a pool of people. Patients and the public in general have a right to privacy and the right to choose what data, if any, they would like to share. Data breaches now make it possible for patient data to fall into the hands of the insurance companies resulting in a denial of medical insurance because a patient is deemed more expensive by the insurance provider due to their genetic composition. Patient privacy leads to restricted availability of data, which leads to limited model training and therefore the full potential of a model is not explored.

Artificially intelligent computer systems are used extensively in medical sciences. Common applications include diagnosing patients, end-to-end drug discovery and development, improving communication between physician and patient, transcribing medical documents, such as prescriptions, and remotely treating patients. While computer systems often execute tasks more efficiently than humans, more recently, state-of-the-art computer algorithms have achieved accuracies which are at par with human experts in the field of medical sciences. Some speculate that it is only a matter of time before humans are completely replaced in certain roles within the medical sciences. The motivation of this article is to discuss the ways in which artificial intelligence is changing the landscape of medical science and to separate hype from reality.

Introduction

Artificial intelligence (AI) in varying forms and degrees has been used to develop and advance a wide spectrum of fields, such as banking and financial markets, education, supply chains, manufacturing, retail and e-commerce, and healthcare. Within the technology industry, AI has been an important enabler for many new business innovations. These include web search (e.g., Google), content recommendations (e.g., Netflix), product recommendations (e.g., Amazon), targeted advertising (e.g., Facebook), and autonomous vehicles (e.g., Tesla).

Humans reap the benefits of artificially intelligent systems every day. Starting from the spam free emails that we receive in our inboxes, to smart watches that use inputs from accelerometer sensors to distinguish between mundane activities and aerobic activity, to buying products on online shopping sites, like Amazon that recommend products based on our previous purchase records. These examples represent the use of AI in a variety of fields, such as technology and retail. AI has transformed our everyday lives, with an effect on the way we perceive and process information.

This article aims to present various aspects of AI as it pertains to the medical sciences. The article will focus on past and present day applications in the medical sciences and showcase companies that currently use artificially intelligent systems in the healthcare industry. Furthermore, this article will conclude by highlighting the critical importance of interdisciplinary collaboration resulting in the creation of ethical, unbiased artificially intelligent systems.

What is AI?

AI is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Some applications of AI include automated interfaces for visual perception, speech recognition, decision-making, and translation between languages. AI is an interdisciplinary science.[ 1 ]

It is widely accepted that the term AI was first coined in 1956 when American computer scientist John McCarthy et al . organized the Dartmouth Conference.[ 2 ] Prior to that, work in the field of AI included the Turing test proposed by Alan Turing[ 3 ] as a measure of machine intelligence and a chess-playing program written by Dietrich Prinz.[ 4 ]

Artificially intelligent systems in healthcare have the following typical pattern. Such a system starts with a large amount of data, on these data machine-learning algorithms are employed to gain information, this information is then used to generate a useful output to solve a well-defined problem in the medical system. Figure 1 captures the typical workflow of an AI solution. Applications of AI in the field of medical sciences include matching patient symptoms to appropriate physician,[ 5 ] patient diagnosis,[ 6 ] patient prognosis,[ 7 ] drug discovery,[ 8 , 9 ] bot assistant that can translate languages,[ 10 ] transcribe notes, and organize images and files.[ 11 ]

Illustration outlining the development of an artificially intelligent model

History of AI in Medical Field

Great advances have been made in using artificially intelligent systems in case of patient diagnosis. For example, in the field of visually oriented specialties, such as dermatology,[ 12 , 13 ] clinical imaging data has been used by Esteva et al .[ 6 ] and Hekler et al .[ 14 ] to develop classification models to aid physicians in the diagnosis of skin cancer, skin lesions, and psoriasis. In particular, Esteva et al .,[ 6 ] trained a deep convolutional neural network (DCNN) model using 129,450 images to classify images into one of two categories (also known as binary classification problem in machine learning) as either keratinocyte carcinoma or seborrheic keratosis; and malignant melanoma or benign nevus. They further established that the DCNN achieved performance at par to that of 21 board-certified dermatologists. Their research demonstrated that AI systems were capable of classifying skin cancers with a level of competence comparable to dermatologists and required only a fraction of the time to train the model in comparison to physicians who spend years in medical school and also relied on experience they developed through patient diagnosis over decades.

Much work has also been done in the realm of AI and patient prognosis. For instance, researchers at Google[ 7 ] developed and trained a DCNN using 128,175 retinal fundus images to classify images as diabetic retinopathy and macular edema for adults with diabetes. There are several advantages of the existence of such an artificially intelligent model, such as:

• Automated grading of diabetic retinopathy leading to increased efficiency in diagnosing many patients in shorter time;
• Serving as a second opinion opthalmologists;
• Detection of diabetic retinopathy in early stages due to capability of the model to study images at the granular level-something impossible for a human opthalmologist to do;
• Vast coverage of screening programs reducing barriers to access.

Huge strides have been made in application of AI systems to drug discovery[ 15 ] and providing personalized treatment options.[ 16 ] Companies, such as Verge Genomics, focus on the application of machine-learning algorithms to analyze human genomic data and identify drugs to combat neurological diseases, such as Parkinson's, Alzheimer's, and amyotrophic lateral sclerosis (ALS) in a cost-effective way.

Artificially intelligent systems are also being applied in the healthcare sector to enhance patient experience, patient care, and provide support to physicians through the use of AI assistants. Companies, such as BotMD have built systems that can help 24 h with clinical related issues regarding:

• Instantly finding which physicians are on call and scheduling the next available appointment; the AI system can also search multiple scheduling systems across different hospitals
• Answering prescription related questions, like drug availability and cost-effective alternative drugs
• Assisting doctors search hospital protocol, list of available clinical tools, and available drugs all through the use of a mobile application, thus improving workflow in the hospital.

Companies Using AI in Medical Sciences

Table 1 below lists just a few of hundreds of companies in the field of technology, healthcare, and pharmacies that conduct research on artificially intelligent systems and their applications in the healthcare industry. Additionally, applications of artificially intelligent systems in healthcare can be broadly classified into three categories[ 17 ] (for the companies in Table 1 , the type of AI system is also noted):

Some major companies around the world using artificial intelligence in medical sciences

• Patient-oriented AI
• Clinician-oriented AI and
• Administrative and Operational-oriented AI.

Present Day Use of AI

The most recent application of AI in global healthcare is the prediction of emerging hotspots using contact tracing, and flight traveler data to fight off the novel coronavirus (COVID-19) pandemic.

Contact tracing is a disease control measure used by government authorities to limit spread of a disease. Contact tracing works by contacting and informing individuals that have been exposed to a person who has contracted the disease and instructing them to quarantine to prevent further spread of the disease. As reported by Apple Newsroom,[ 18 ] tech giants like Google and Apple have joined forces to create a contact tracing platform that will use artificial intelligent systems through the use of application programming interfaces commonly referred to as API's on smartphones. The platform will enable users who choose to enroll to report their lab results. Location services will then allow the platform to contact people who may have been in the vicinity of the infected person.

Canadian company BlueDot creates outbreak risk software that mitigates exposure to infectious diseases.[ 19 ] BlueDot published the first scientific paper[ 20 ] on COVID-19 that accurately predicted the global spread of the virus. The company uses techniques such as natural language processing (NLP), machine learning (ML), along with automated infectious disease surveillance by analyzing approximately 100,000 articles from over 65 countries every day, travel itinerary information and flight paths, an area's climate, temperature and even local livestock to help predict future outbreaks.

Myth Versus Reality in AI

There is a lot of hope that AI will be able to advance the healthcare sector in a variety of ways, not just for patient diagnosis, patient prognosis, drug discovery, but also to serve as an assistant for physician and provide a better and more personalized experience for patients. This hope has been fueled by some successful applications of AI in healthcare. Side-by-side however, there are unrealistic expectations of what AI can do and what the landscape of the healthcare industry will look like in the future.

Dr. Anthony Chang was one of 2019's invited speakers for the Society for Artificial Intelligence in Medicine (AIME) conference held in Poznan, Poland, where he presented a lecture entitled: Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective. Below we list two of the more common myths regarding the application of artificially intelligent systems in healthcare.

While nobody can entirely predict the future, the fact is that physicians who understand the role of AI in healthcare will likely have an advantage in their career. For instance, the American College of Radiology (ACR) posted a job advertisement for a Radiologist:

https://jobs.acr.org/job/radiologist-for-teleradiology- ai-practice/50217408/

listing two requirements for the job:

• Must be American Board of Radiology Certified
• Must be enthusiastic, well-trained radiologist excited about a future where radiologists are supported by world-class AI and machine learning.

The use of AI in any field of study consists of many components and programming is just one of them. For the continued growth, development and success of AI applications in healthcare, physicians and data scientists need to continue collaboration to build meaningful AI systems. Physicians need to understand what AI is capable of achieving and need to evaluate how their role can be improved with AI. Physicians need to communicate this information to data scientists who can then build an AI system. The collaboration does not end here. Together physicians and data scientists must figure out what kind of data they have available to use for model training and, further, once the model is built its performance must be analyzed and interpreted, both of which require collaboration between physicians and data scientists. A further trend is the significant commoditization of AI software. For instance, today it is possible to use a visual tool (requiring no coding) to build a visual classifier. An example of such a tool is Teachable Machine by Google.

Limitations and Challenges in the Application of Artificially Intelligent Systems in Medical Science

The application of artificially intelligent systems in any field including healthcare comes with its share of limitations and challenges. The time has come to change our mindset from being reactive to being proactive with regard to downfalls of new technology. Here we discuss those challenges focusing more on those that pertain particularly to healthcare.

Availability of data

Creating biased models.

Biased data

Artificially intelligent systems are then trained with a portion of the data that was collected (also known as training data set) with the remaining data reserved for testing (also known as testing data set). Thus, if the data collected is biased, that is, it targets a particular race, a particular gender, a specific age group then the resulting model will be biased. Thus the data collected must be a true representation of the population for which its use is intended.

Data preprocessing

Even after unbiased data has been collected, it is still possible to create a biased model. The collected data must be preprocessed before it can be used to train an algorithm. The raw data that has been collected often contains errors due to manual entry of data or a variety of other reasons. These entries are sometimes modified through mathematical justification or are simply removed. Care should be taken that data preprocessing does not result in a biased pool of data.

Model selection

With the existence of several algorithms and models to choose from, one must select the algorithm that is best suited for the task at hand. Thus, the process of model selection is extremely important. Bias models are ones that are overly simple and fail to capture the trends present in the dataset.

Presenting biased models

It is important for a user of an artificially intelligent system to have a basic understanding of how such models are built. This way a user can better interpret the output of the model and decide how to make use of the output. For instance, there are many metrics that one could use to evaluate the performance of a model, such as accuracy, precision, recall, F 1 score , and AUC score .[ 21 ] However, not every metric is appropriate for every problem. When the user of an artificially intelligent system is presented with performance metrics of a model, they need to make sure that the metrics appropriate to the problem are being presented and not just the metrics with the highest scores.

Fragmented data

Another limitation of the application of AI is that models that one organization spends time and effort to design and deploy for a specific task (regression, classification, clustering, NLP, etc) cannot be seamlessly transitioned for immediate use to another organization without recalibration. Due to privacy concerns, data sharing is often inaccessible or limited between healthcare organizations resulting in fragmented data limiting the reliability of a model.

Artificial Intelligent systems have a reputation of being blackboxes due to the complexity of the mathematical algorithms involved. There is a need to make models more accessible and interpretable. While there is some recent work in this direction, there is still some progress to be made.[ 23 ]

Conclusion: The Future of AI in Medical Sciences

Despite the above limitations, AI looks well positioned to revolutionize the healthcare industry. AI systems can help free up the time for busy doctors by transcribing notes, entering and organizing patient data into portals (such as EPIC) and diagnosing patients, potentially serving as a means for providing a second opinion for physicians. Artificially intelligent systems can also help patients with follow-up care and availability of prescription drug alternatives. AI also has the capability of remotely diagnosing patients, thus extending medical services to remote areas, beyond the major urban centers of the world. The future of AI in healthcare is bright and promising, and yet much remains to be done.

The application of artificially intelligent systems in healthcare for use by the general public is relatively unexplored. Only recently the FDA (U.S Food and Drug Administration) approved AliveCor's Kardiaband (in 2017) and Apple's smartwatch series 4 (in 2018) to detect atrial fibrillation. The use of a smartwatch is a first step toward empowering people to collect personal health data, and enable rapid interventions from the patient's medical support teams.

There are many negative effects of modern technology on mental health. However, researchers at the University of Southern California (USC) in collaboration with Defense Advanced Research Projects Agency and the U.S. Army found that people suffering from post-traumatic stress and other forms of mental anguish are more open to discussing their concerns with virtual humans than actual humans for fear of judgment. This research[ 23 ] has promising results for the role of virtual assistants resulting in the collection of honest answers from patients that could help doctors diagnose and treat their patients more appropriately and with better information.

Most global pharmaceutical companies have invested their time and money on using AI for drug development of major diseases, such as cancer or cardiovascular disease. However, development of models for diagnosing neglected tropical diseases (malaria and tuberculosis) and rare diseases remains largely unexplored. The FDA now incentivizes companies to develop new treatments for these diseases through priority vouchers.[ 24 ]

Given the impact that AI and machine learning is having on our wider world, it is important for AI to be a part of the curriculum for a range of domain experts. This is particularly true for the medical profession, where the cost of a wrong decision can be fatal. As identified here, there is a lot of nuance in how an AI system is built. Understanding this process and the choices it entails are important for appropriate usage of this automated system. The data used to learn from and the optimization strategy used has a deep impact on the applicability of the AI system to solve a particular problem. An understanding and appreciation of these design decisions is important for medical profession.

AI has the potential to help fix many of healthcare's biggest problems but we are still far from making this a reality. One big problem and barrier from making this a reality is data. We can invent all the promising technologies and machine learning algorithms but without sufficient and well represented data, we cannot realize the full potential of AI in healthcare. The healthcare industry needs to digitize medical records, it needs to come together to agree on the standardization of the data infrastructure, it needs to create an iron-clad system to protect the confidentiality and handle consent of data from patients. Without these radical changes and collaboration in the healthcare industry, it would be challenging to achieve the true promise of AI to help human health.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

• Research article
• Open access
• Published: 10 April 2021

The role of artificial intelligence in healthcare: a structured literature review

• Silvana Secinaro 1 ,
• Davide Calandra 1 ,
• Aurelio Secinaro 2 ,
• Vivek Muthurangu 3 &
• Paolo Biancone 1  

BMC Medical Informatics and Decision Making volume  21 , Article number:  125 ( 2021 ) Cite this article

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Background/Introduction

Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions.

The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package.

The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths.

Conclusions

The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Peer Review reports

Artificial intelligence (AI) generally applies to computational technologies that emulate mechanisms assisted by human intelligence, such as thought, deep learning, adaptation, engagement, and sensory understanding [ 1 , 2 ]. Some devices can execute a role that typically involves human interpretation and decision-making [ 3 , 4 ]. These techniques have an interdisciplinary approach and can be applied to different fields, such as medicine and health. AI has been involved in medicine since as early as the 1950s, when physicians made the first attempts to improve their diagnoses using computer-aided programs [ 5 , 6 ]. Interest and advances in medical AI applications have surged in recent years due to the substantially enhanced computing power of modern computers and the vast amount of digital data available for collection and utilisation [ 7 ]. AI is gradually changing medical practice. There are several AI applications in medicine that can be used in a variety of medical fields, such as clinical, diagnostic, rehabilitative, surgical, and predictive practices. Another critical area of medicine where AI is making an impact is clinical decision-making and disease diagnosis. AI technologies can ingest, analyse, and report large volumes of data across different modalities to detect disease and guide clinical decisions [ 3 , 8 ]. AI applications can deal with the vast amount of data produced in medicine and find new information that would otherwise remain hidden in the mass of medical big data [ 9 , 10 , 11 ]. These technologies can also identify new drugs for health services management and patient care treatments [ 5 , 6 ].

Courage in the application of AI is visible through a search in the primary research databases. However, as Meskò et al. [ 7 ] find, the technology will potentially reduce care costs and repetitive operations by focusing the medical profession on critical thinking and clinical creativity. As Cho et al. and Doyle et al. [ 8 , 9 ] add, the AI perspective is exciting; however, new studies will be needed to establish the efficacy and applications of AI in the medical field [ 10 ].

Our paper will also concentrate on AI strategies for healthcare from the accounting, business, and management perspectives. The authors used the structured literature review (SLR) method for its reliable and replicable research protocol [ 11 ] and selected bibliometric variables as sources of investigation. Bibliometric usage enables the recognition of the main quantitative variables of the study stream [ 12 ]. This method facilitates the detection of the required details of a particular research subject, including field authors, number of publications, keywords for interaction between variables (policies, properties and governance) and country data [ 13 ]. It also allows the application of the science mapping technique [ 14 ]. Our paper adopted the Bibliometrix R package and the biblioshiny web interface as tools of analysis [ 14 ].

The investigation offers the following insights for future researchers and practitioners:

bibliometric information on 288 peer-reviewed English papers from the Scopus collection.

Identification of leading journals in this field, such as Journal of Medical Systems, Studies in Health Technology and Informatics, IEEE Journal of Biomedical and Health Informatics, and Decision Support Systems.

Qualitative and quantitative information on authors’ Lotka’s law, h-index, g-index, m-index, keyword, and citation data.

Research on specific countries to assess AI in the delivery and effectiveness of healthcare, quotes, and networks within each region.

A topic dendrogram study that identifies five research clusters: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making.

An in-depth discussion that develops theoretical and practical implications for future studies.

The paper is organised as follows. Section  2 lists the main bibliometric articles in this field. Section  3 elaborates on the methodology. Section  4 presents the findings of the bibliometric analysis. Section  5 discusses the main elements of AI in healthcare based on the study results. Section  6 concludes the article with future implications for research.

Related works and originality

As suggested by Zupic and Čater [ 15 ], a research stream can be evaluated with bibliometric methods that can introduce objectivity and mitigate researcher bias. For this reason, bibliometric methods are attracting increasing interest among researchers as a reliable and impersonal research analytical approach [ 16 , 17 ]. Recently, bibliometrics has been an essential method for analysing and predicting research trends [ 18 ]. Table  1 lists other research that has used a similar approach in the research stream investigated.

The scientific articles reported show substantial differences in keywords and research topics that have been previously studied. The bibliometric analysis of Huang et al. [ 19 ] describes rehabilitative medicine using virtual reality technology. According to the authors, the primary goal of rehabilitation is to enhance and restore functional ability and quality of life for patients with physical impairments or disabilities. In recent years, many healthcare disciplines have been privileged to access various technologies that provide tools for both research and clinical intervention.

Hao et al. [ 20 ] focus on text mining in medical research. As reported, text mining reveals new, previously unknown information by using a computer to automatically extract information from different text resources. Text mining methods can be regarded as an extension of data mining to text data. Text mining is playing an increasingly significant role in processing medical information. Similarly, the studies by dos Santos et al. [ 21 ] focus on applying data mining and machine learning (ML) techniques to public health problems. As stated in this research, public health may be defined as the art and science of preventing diseases, promoting health, and prolonging life. Using data mining and ML techniques, it is possible to discover new information that otherwise would be hidden. These two studies are related to another topic: medical big data. According to Liao et al. [ 22 ], big data is a typical “buzzword” in the business and research community, referring to a great mass of digital data collected from various sources. In the medical field, we can obtain a vast amount of data (i.e., medical big data). Data mining and ML techniques can help deal with this information and provide helpful insights for physicians and patients. More recently, Choudhury et al. [ 23 ] provide a systematic review on the use of ML to improve the care of elderly patients, demonstrating eligible studies primarily in psychological disorders and eye diseases.

Tran et al. [ 2 ] focus on the global evolution of AI research in medicine. Their bibliometric analysis highlights trends and topics related to AI applications and techniques. As stated in Connelly et al.’s [ 24 ] study, robot-assisted surgeries have rapidly increased in recent years. Their bibliometric analysis demonstrates how robotic-assisted surgery has gained acceptance in different medical fields, such as urological, colorectal, cardiothoracic, orthopaedic, maxillofacial and neurosurgery applications. Additionally, the bibliometric analysis of Guo et al. [ 25 ] provides an in-depth study of AI publications through December 2019. The paper focuses on tangible AI health applications, giving researchers an idea of how algorithms can help doctors and nurses. A new stream of research related to AI is also emerging. In this sense, Choudhury and Asan’s [ 26 ] scientific contribution provides a systematic review of the AI literature to identify health risks for patients. They report on 53 studies involving technology for clinical alerts, clinical reports, and drug safety. Considering the considerable interest within this research stream, this analysis differs from the current literature for several reasons. It aims to provide in-depth discussion, considering mainly the business, management, and accounting fields and not dealing only with medical and health profession publications.

Additionally, our analysis aims to provide a bibliometric analysis of variables such as authors, countries, citations and keywords to guide future research perspectives for researchers and practitioners, as similar analyses have done for several publications in other research streams [ 15 , 16 , 27 ]. In doing so, we use a different database, Scopus, that is typically adopted in social sciences fields. Finally, our analysis will propose and discuss a dominant framework of variables in this field, and our analysis will not be limited to AI application descriptions.

Methodology

This paper evaluated AI in healthcare research streams using the SLR method [ 11 ]. As suggested by Massaro et al. [ 11 ], an SLR enables the study of the scientific corpus of a research field, including the scientific rigour, reliability and replicability of operations carried out by researchers. As suggested by many scholars, the methodology allows qualitative and quantitative variables to highlight the best authors, journals and keywords and combine a systematic literature review and bibliometric analysis [ 27 , 28 , 29 , 30 ]. Despite its widespread use in business and management [ 16 , 31 ], the SLR is also used in the health sector based on the same philosophy through which it was originally conceived [ 32 , 33 ]. A methodological analysis of previously published articles reveals that the most frequently used steps are as follows [ 28 , 31 , 34 ]:

defining research questions;

writing the research protocol;

defining the research sample to be analysed;

developing codes for analysis; and

critically analysing, discussing, and identifying a future research agenda.

Considering the above premises, the authors believe that an SLR is the best method because it combines scientific validity, replicability of the research protocol and connection between multiple inputs.

As stated by the methodological paper, the first step is research question identification. For this purpose, we benefit from the analysis of Zupic and Čater [ 15 ], who provide several research questions for future researchers to link the study of authors, journals, keywords and citations. Therefore, RQ1 is “What are the most prominent authors, journal keywords and citations in the field of the research study?” Additionally, as suggested by Haleem et al. [ 35 ], new technologies, including AI, are changing the medical field in unexpected timeframes, requiring studies in multiple areas. Therefore, RQ2 is “How does artificial intelligence relate to healthcare, and what is the focus of the literature?” Then, as discussed by Massaro et al. [ 36 ], RQ3 is “What are the research applications of artificial intelligence for healthcare?”.

The first research question aims to define the qualitative and quantitative variables of the knowledge flow under investigation. The second research question seeks to determine the state of the art and applications of AI in healthcare. Finally, the third research question aims to help researchers identify practical and theoretical implications and future research ideas in this field.

The second fundamental step of the SLR is writing the research protocol [ 11 ]. Table  2 indicates the currently known literature elements, uniquely identifying the research focus, motivations and research strategy adopted and the results providing a link with the following points. Additionally, to strengthen the analysis, our investigation benefits from the PRISMA statement methodological article [ 37 ]. Although the SLR is a validated method for systematic reviews and meta-analyses, we believe that the workflow provided may benefit the replicability of the results [ 37 , 38 , 39 , 40 ]. Figure  1 summarises the researchers’ research steps, indicating that there are no results that can be referred to as a meta-analysis.

Source : Authors’ elaboration on Liberati et al. [ 37 ]

PRISMA workflow.

The third step is to specify the search strategy and search database. Our analysis is based on the search string “Artificial Intelligence” OR “AI” AND “Healthcare” with a focus on “Business, Management, and Accounting”, “Decision Sciences”, and “Health professions”. As suggested by [ 11 , 41 ] and motivated by [ 42 ], keywords can be selected through a top-down approach by identifying a large search field and then focusing on particular sub-topics. The paper uses data retrieved from the Scopus database, a multi-disciplinary database, which allowed the researchers to identify critical articles for scientific analysis [ 43 ]. Additionally, Scopus was selected based on Guo et al.’s [ 25 ] limitations, which suggest that “future studies will apply other databases, such as Scopus, to explore more potential papers” . The research focuses on articles and reviews published in peer-reviewed journals for their scientific relevance [ 11 , 16 , 17 , 29 ] and does not include the grey literature, conference proceedings or books/book chapters. Articles written in any language other than English were excluded [ 2 ]. For transparency and replicability, the analysis was conducted on 11 January 2021. Using this research strategy, the authors retrieved 288 articles. To strengthen the study's reliability, we publicly provide the full bibliometric extract on the Zenodo repository [ 44 , 45 ].

The fourth research phase is defining the code framework that initiates the analysis of the variables. The study will identify the following:

descriptive information of the research area;

source analysis [ 16 ];

author and citation analysis [ 28 ];

keywords and network analysis [ 14 ]; and

geographic distribution of the papers [ 14 ].

The final research phase is the article’s discussion and conclusion, where implications and future research trends will be identified.

At the research team level, the information is analysed with the statistical software R-Studio and the Bibliometrix package [ 15 ], which allows scientific analysis of the results obtained through the multi-disciplinary database.

The analysis of bibliometric results starts with a description of the main bibliometric statistics with the aim of answering RQ1, What are the most prominent authors, journal keywords and citations in the field of the research study?, and RQ2, How does artificial intelligence relate to healthcare, and what is the focus of the literature? Therefore, the following elements were thoroughly analysed: (1) type of document; (2) annual scientific production; (3) scientific sources; (4) source growth; (5) number of articles per author; (6) author’s dominance ranking; (7) author’s h-index, g-index, and m-index; (8) author’s productivity; (9) author’s keywords; (10) topic dendrogram; (11) a factorial map of the document with the highest contributions; (12) article citations; (13) country production; (14) country citations; (15) country collaboration map; and (16) country collaboration network.

Main information

Table  3 shows the information on 288 peer-reviewed articles published between 1992 and January 2021 extracted from the Scopus database. The number of keywords is 946 from 136 sources, and the number of keywords plus, referring to the number of keywords that frequently appear in an article’s title, was 2329. The analysis period covered 28 years and 1 month of scientific production and included an annual growth rate of 5.12%. However, the most significant increase in published articles occurred in the past three years (please see Fig.  2 ). On average, each article was written by three authors (3.56). Finally, the collaboration index (CI), which was calculated as the total number of authors of multi-authored articles/total number of multi-authored articles, was 3.97 [ 46 ].

Source : Authors’ elaboration

Annual scientific production.

Table  4 shows the top 20 sources related to the topic. The Journal of Medical Systems is the most relevant source, with twenty-one of the published articles. This journal's main issues are the foundations, functionality, interfaces, implementation, impacts, and evaluation of medical technologies. Another relevant source is Studies in Health Technology and Informatics, with eleven articles. This journal aims to extend scientific knowledge related to biomedical technologies and medical informatics research. Both journals deal with cloud computing, machine learning, and AI as a disruptive healthcare paradigm based on recent publications. The IEEE Journal of Biomedical and Health Informatics investigates technologies in health care, life sciences, and biomedicine applications from a broad perspective. The next journal, Decision Support Systems, aims to analyse how these technologies support decision-making from a multi-disciplinary view, considering business and management. Therefore, the analysis of the journals revealed that we are dealing with an interdisciplinary research field. This conclusion is confirmed, for example, by the presence of purely medical journals, journals dedicated to the technological growth of healthcare, and journals with a long-term perspective such as futures.

The distribution frequency of the articles (Fig.  3 ) indicates the journals dealing with the topic and related issues. Between 2008 and 2012, a significant growth in the number of publications on the subject is noticeable. However, the graph shows the results of the Loess regression, which includes the quantity and publication time of the journal under analysis as variables. This method allows the function to assume an unlimited distribution; that is, feature can consider values below zero if the data are close to zero. It contributes to a better visual result and highlights the discontinuity in the publication periods [ 47 ].

Source growth. Source : Authors’ elaboration

Finally, Fig.  4 provides an analytical perspective on factor analysis for the most cited papers. As indicated in the literature [ 48 , 49 ], using factor analysis to discover the most cited papers allows for a better understanding of the scientific world’s intellectual structure. For example, our research makes it possible to consider certain publications that effectively analyse subject specialisation. For instance, Santosh’s [ 50 ] article addresses the new paradigm of AI with ML algorithms for data analysis and decision support in the COVID-19 period, setting a benchmark in terms of citations by researchers. Moving on to the application, an article by Shickel et al. [ 51 ] begins with the belief that the healthcare world currently has much health and administrative data. In this context, AI and deep learning will support medical and administrative staff in extracting data, predicting outcomes, and learning medical representations. Finally, in the same line of research, Baig et al. [ 52 ], with a focus on wearable patient monitoring systems (WPMs), conclude that AI and deep learning may be landmarks for continuous patient monitoring and support for healthcare delivery.

Factorial map of the most cited documents.

This section identifies the most cited authors of articles on AI in healthcare. It also identifies the authors’ keywords, dominance factor (DF) ranking, h-index, productivity, and total number of citations. Table  5 identifies the authors and their publications in the top 20 rankings. As the table shows, Bushko R.G. has the highest number of publications: four papers. He is the editor-in-chief of Future of Health Technology, a scientific journal that aims to develop a clear vision of the future of health technology. Then, several authors each wrote three papers. For instance, Liu C. is a researcher active in the topic of ML and computer vision, and Sharma A. from Emory University Atlanta in the USA is a researcher with a clear focus on imaging and translational informatics. Some other authors have two publications each. While some authors have published as primary authors, most have published as co-authors. Hence, in the next section, we measure the contributory power of each author by investigating the DF ranking through the number of elements.

Authors’ dominance ranking

The dominance factor (DF) is a ratio measuring the fraction of multi-authored articles in which an author acts as the first author [ 53 ]. Several bibliometric studies use the DF in their analyses [ 46 , 54 ]. The DF ranking calculates an author’s dominance in producing articles. The DF is calculated by dividing the number of an author’s multi-authored papers as the first author (Nmf) by the author's total number of multi-authored papers (Nmt). This is omitted in the single-author case due to the constant value of 1 for single-authored articles. This formulation could lead to some distortions in the results, especially in fields where the first author is entered by surname alphabetical order [ 55 ].

The mathematical equation for the DF is shown as:

Table  6 lists the top 20 DF rankings. The data in the table show a low level of articles per author, either for first-authored or multi-authored articles. The results demonstrate that we are dealing with an emerging topic in the literature. Additionally, as shown in the table, Fox J. and Longoni C. are the most dominant authors in the field.

Authors’ impact

Table  7 shows the impact of authors in terms of the h-index [ 56 ] (i.e., the productivity and impact of citations of a researcher), g-index [ 57 ] (i.e., the distribution of citations received by a researcher's publications), m-index [ 58 ] (i.e., the h-index value per year), total citations, total paper and years of scientific publication. The H-index was introduced in the literature as a metric for the objective comparison of scientific results and depended on the number of publications and their impact [ 59 ]. The results show that the 20 most relevant authors have an h-index between 2 and 1. For the practical interpretation of the data, the authors considered data published by the London School of Economics [ 60 ]. In the social sciences, the analysis shows values of 7.6 for economic publications by professors and researchers who had been active for several years. Therefore, the youthfulness of the research area has attracted young researchers and professors. At the same time, new indicators have emerged over the years to diversify the logic of the h-index. For example, the g-index indicates an author's impact on citations, considering that a single article can generate these. The m-index, on the other hand, shows the cumulative value over the years.

The analysis, also considering the total number of citations, the number of papers published and the year of starting to publish, thus confirms that we are facing an expanding research flow.

Authors’ productivity

Figure  5 shows Lotka’s law. This mathematical formulation originated in 1926 to describe the publication frequency by authors in a specific research field [ 61 ]. In practice, the law states that the number of authors contributing to research in a given period is a fraction of the number who make up a single contribution [ 14 , 61 ].

Lotka’s law.

The mathematical relationship is expressed in reverse in the following way:

where y x is equal to the number of authors producing x articles in each research field. Therefore, C and n are constants that can be estimated in the calculation.

The figure's results are in line with Lotka's results, with an average of two publications per author in a given research field. In addition, the figure shows the percentage of authors. Our results lead us to state that we are dealing with a young and growing research field, even with this analysis. Approximately 70% of the authors had published only their first research article. Only approximately 20% had published two scientific papers.

Authors’ keywords

This section provides information on the relationship between the keywords artificial intelligence and healthcare . This analysis is essential to determine the research trend, identify gaps in the discussion on AI in healthcare, and identify the fields that can be interesting as research areas [ 42 , 62 ].

Table  8 highlights the total number of keywords per author in the top 20 positions. The ranking is based on the following elements: healthcare, artificial intelligence, and clinical decision support system . Keyword analysis confirms the scientific area of reference. In particular, we deduce the definition as “Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” [ 2 , 63 ]. Panch et al. [ 4 ] find that these technologies can be used in different business and management areas. After the first keyword, the analysis reveals AI applications and related research such as machine learning and deep learning.

Additionally, data mining and big data are a step forward in implementing exciting AI applications. According to our specific interest, if we applied AI in healthcare, we would achieve technological applications to help and support doctors and medical researchers in decision-making. The link between AI and decision-making is the reason why we find, in the seventh position, the keyword clinical decision support system . AI techniques can unlock clinically relevant information hidden in the massive amount of data that can assist clinical decision-making [ 64 ]. If we analyse the following keywords, we find other elements related to decision-making and support systems.

The TreeMap below (Fig.  6 ) highlights the combination of possible keywords representing AI and healthcare.

Keywords treemap.

The topic dendrogram in Fig.  7 represents the hierarchical order and the relationship between the keywords generated by hierarchical clustering [ 42 ]. The cut in the figure and the vertical lines facilitate an investigation and interpretation of the different clusters. As stated by Andrews [ 48 ], the figure is not intended to find the perfect level of associations between clusters. However, it aims to estimate the approximate number of clusters to facilitate further discussion.

Topic dendrogram.

The research stream of AI in healthcare is divided into two main strands. The blue strand focuses on medical information systems and the internet. Some papers are related to healthcare organisations, such as the Internet of Things, meaning that healthcare organisations use AI to support health services management and data analysis. AI applications are also used to improve diagnostic and therapeutic accuracy and the overall clinical treatment process [ 2 ]. If we consider the second block, the red one, three different clusters highlight separate aspects of the topic. The first could be explained as AI and ML predictive algorithms. Through AI applications, it is possible to obtain a predictive approach that can ensure that patients are better monitored. This also allows a better understanding of risk perception for doctors and medical researchers. In the second cluster, the most frequent words are decisions , information system , and support system . This means that AI applications can support doctors and medical researchers in decision-making. Information coming from AI technologies can be used to consider difficult problems and support a more straightforward and rapid decision-making process. In the third cluster, it is vital to highlight that the ML model can deal with vast amounts of data. From those inputs, it can return outcomes that can optimise the work of healthcare organisations and scheduling of medical activities.

Furthermore, the word cloud in Fig.  8 highlights aspects of AI in healthcare, such as decision support systems, decision-making, health services management, learning systems, ML techniques and diseases. The figure depicts how AI is linked to healthcare and how it is used in medicine.

Word cloud.

Figure  9 represents the search trends based on the keywords analysed. The research started in 2012. First, it identified research topics related to clinical decision support systems. This topic was recurrent during the following years. Interestingly, in 2018, studies investigated AI and natural language processes as possible tools to manage patients and administrative elements. Finally, a new research stream considers AI's role in fighting COVID-19 [ 65 , 66 ].

Keywords frequency.

Table  9 represents the number of citations from other articles within the top 20 rankings. The analysis allows the benchmark studies in the field to be identified [ 48 ]. For instance, Burke et al. [ 67 ] writes the most cited paper and analyses efficient nurse rostering methodologies. The paper critically evaluates tangible interdisciplinary solutions that also include AI. Immediately thereafter, Ahmed M.A.'s article proposes a data-driven optimisation methodology to determine the optimal number of healthcare staff to optimise patients' productivity [ 68 ]. Finally, the third most cited article lays the groundwork for developing deep learning by considering diverse health and administrative information [ 51 ].

This section analyses the diffusion of AI in healthcare around the world. It highlights countries to show the geographies of this research. It includes all published articles, the total number of citations, and the collaboration network. The following sub-sections start with an analysis of the total number of published articles.

Country total articles

Figure  9 and Table  10 display the countries where AI in healthcare has been considered. The USA tops the list of countries with the maximum number of articles on the topic (215). It is followed by China (83), the UK (54), India (51), Australia (54), and Canada (32). It is immediately evident that the theme has developed on different continents, highlighting a growing interest in AI in healthcare. The figure shows that many areas, such as Russia, Eastern Europe and Africa except for Algeria, Egypt, and Morocco, have still not engaged in this scientific debate.

Country publications and collaboration map

This section discusses articles on AI in healthcare in terms of single or multiple publications in each country. It also aims to observe collaboration and networking between countries. Table  11 and Fig.  10 highlight the average citations by state and show that the UK, the USA, and Kuwait have a higher average number of citations than other countries. Italy, Spain and New Zealand have the most significant number of citations.

Articles per country.

Figure  11 depicts global collaborations. The blue colour on the map represents research cooperation among nations. Additionally, the pink border linking states indicates the extent of collaboration between authors. The primary cooperation between nations is between the USA and China, with two collaborative articles. Other collaborations among nations are limited to a few papers.

Collaboration map.

Artificial intelligence for healthcare: applications

This section aims to strengthen the research scope by answering RQ3: What are the research applications of artificial intelligence for healthcare?

Benefiting from the topical dendrogram, researchers will provide a development model based on four relevant variables [ 69 , 70 ]. AI has been a disruptive innovation in healthcare [ 4 ]. With its sophisticated algorithms and several applications, AI has assisted doctors and medical professionals in the domains of health information systems, geocoding health data, epidemic and syndromic surveillance, predictive modelling and decision support, and medical imaging [ 2 , 9 , 10 , 64 ]. Furthermore, the researchers considered the bibliometric analysis to identify four macro-variables dominant in the field and used them as authors' keywords. Therefore, the following sub-sections aim to explain the debate on applications in healthcare for AI techniques. These elements are shown in Fig.  12 .

Dominant variables for AI in healthcare.

Health services management

One of the notable aspects of AI techniques is potential support for comprehensive health services management. These applications can support doctors, nurses and administrators in their work. For instance, an AI system can provide health professionals with constant, possibly real-time medical information updates from various sources, including journals, textbooks, and clinical practices [ 2 , 10 ]. These applications' strength is becoming even more critical in the COVID-19 period, during which information exchange is continually needed to properly manage the pandemic worldwide [ 71 ]. Other applications involve coordinating information tools for patients and enabling appropriate inferences for health risk alerts and health outcome prediction [ 72 ]. AI applications allow, for example, hospitals and all health services to work more efficiently for the following reasons:

Clinicians can access data immediately when they need it.

Nurses can ensure better patient safety while administering medication.

Patients can stay informed and engaged in their care by communicating with their medical teams during hospital stays.

Additionally, AI can contribute to optimising logistics processes, for instance, realising drugs and equipment in a just-in-time supply system based totally on predictive algorithms [ 73 , 74 ]. Interesting applications can also support the training of personnel working in health services. This evidence could be helpful in bridging the gap between urban and rural health services [ 75 ]. Finally, health services management could benefit from AI to leverage the multiplicity of data in electronic health records by predicting data heterogeneity across hospitals and outpatient clinics, checking for outliers, performing clinical tests on the data, unifying patient representation, improving future models that can predict diagnostic tests and analyses, and creating transparency with benchmark data for analysing services delivered [ 51 , 76 ].

Predictive medicine

Another relevant topic is AI applications for disease prediction and diagnosis treatment, outcome prediction and prognosis evaluation [ 72 , 77 ]. Because AI can identify meaningful relationships in raw data, it can support diagnostic, treatment and prediction outcomes in many medical situations [ 64 ]. It allows medical professionals to embrace the proactive management of disease onset. Additionally, predictions are possible for identifying risk factors and drivers for each patient to help target healthcare interventions for better outcomes [ 3 ]. AI techniques can also help design and develop new drugs, monitor patients and personalise patient treatment plans [ 78 ]. Doctors benefit from having more time and concise data to make better patient decisions. Automatic learning through AI could disrupt medicine, allowing prediction models to be created for drugs and exams that monitor patients over their whole lives [ 79 ].

• Clinical decision-making

One of the keyword analysis main topics is that AI applications could support doctors and medical researchers in the clinical decision-making process. According to Jiang et al. [ 64 ], AI can help physicians make better clinical decisions or even replace human judgement in healthcare-specific functional areas. According to Bennett and Hauser [ 80 ], algorithms can benefit clinical decisions by accelerating the process and the amount of care provided, positively impacting the cost of health services. Therefore, AI technologies can support medical professionals in their activities and simplify their jobs [ 4 ]. Finally, as Redondo and Sandoval [ 81 ] find, algorithmic platforms can provide virtual assistance to help doctors understand the semantics of language and learning to solve business process queries as a human being would.

Patient data and diagnostics

Another challenging topic related to AI applications is patient data and diagnostics. AI techniques can help medical researchers deal with the vast amount of data from patients (i.e., medical big data ). AI systems can manage data generated from clinical activities, such as screening, diagnosis, and treatment assignment. In this way, health personnel can learn similar subjects and associations between subject features and outcomes of interest [ 64 ].

These technologies can analyse raw data and provide helpful insights that can be used in patient treatments. They can help doctors in the diagnostic process; for example, to realise a high-speed body scan, it will be simpler to have an overall patient condition image. Then, AI technology can recreate a 3D mapping solution of a patient’s body.

In terms of data, interesting research perspectives are emerging. For instance, we observed the emergence of a stream of research on patient data management and protection related to AI applications [ 82 ].

For diagnostics, AI techniques can make a difference in rehabilitation therapy and surgery. Numerous robots have been designed to support and manage such tasks. Rehabilitation robots physically support and guide, for example, a patient’s limb during motor therapy [ 83 ]. For surgery, AI has a vast opportunity to transform surgical robotics through devices that can perform semi-automated surgical tasks with increasing efficiency. The final aim of this technology is to automate procedures to negate human error while maintaining a high level of accuracy and precision [ 84 ]. Finally, the -19 period has led to increased remote patient diagnostics through telemedicine that enables remote observation of patients and provides physicians and nurses with support tools [ 66 , 85 , 86 ].

This study aims to provide a bibliometric analysis of publications on AI in healthcare, focusing on accounting, business and management, decision sciences and health profession studies. Using the SLR method of Massaro et al. [ 11 ], we provide a reliable and replicable research protocol for future studies in this field. Additionally, we investigate the trend of scientific publications on the subject, unexplored information, future directions, and implications using the science mapping workflow. Our analysis provides interesting insights.

In terms of bibliometric variables, the four leading journals, Journal of Medical Systems , Studies in Health Technology and Informatics , IEEE Journal of Biomedical and Health Informatics , and Decision Support Systems , are optimal locations for the publication of scientific articles on this topic. These journals deal mainly with healthcare, medical information systems, and applications such as cloud computing, machine learning, and AI. Additionally, in terms of h-index, Bushko R.G. and Liu C. are the most productive and impactful authors in this research stream. Burke et al.’s [ 67 ] contribution is the most cited with an analysis of nurse rostering using new technologies such as AI. Finally, in terms of keywords, co-occurrence reveals some interesting insights. For instance, researchers have found that AI has a role in diagnostic accuracy and helps in the analysis of health data by comparing thousands of medical records, experiencing automatic learning with clinical alerts, efficient management of health services and places of care, and the possibility of reconstructing patient history using these data.

Second, this paper finds five cluster analyses in healthcare applications: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making. These technologies can also contribute to optimising logistics processes in health services and allowing a better allocation of resources.

Third, the authors analysing the research findings and the issues under discussion strongly support AI's role in decision support. These applications, however, are demonstrated by creating a direct link to data quality management and the technology awareness of health personnel [ 87 ].

The importance of data quality for the decision-making process

Several authors have analysed AI in the healthcare research stream, but in this case, the authors focus on other literature that includes business and decision-making processes. In this regard, the analysis of the search flow reveals a double view of the literature. On the one hand, some contributions belong to the positivist literature and embrace future applications and implications of technology for health service management, data analysis and diagnostics [ 6 , 80 , 88 ]. On the other hand, some investigations also aim to understand the darker sides of technology and its impact. For example, as Carter [ 89 ] states, the impact of AI is multi-sectoral; its development, however, calls for action to protect personal data. Similarly, Davenport and Kalakota [ 77 ] focus on the ethical implications of using AI in healthcare. According to the authors, intelligent machines raise issues of accountability, transparency, and permission, especially in automated communication with patients. Our analysis does not indicate a marked strand of the literature; therefore, we argue that the discussion of elements such as the transparency of technology for patients is essential for the development of AI applications.

A large part of our results shows that, at the application level, AI can be used to improve medical support for patients (Fig.  11 ) [ 64 , 82 ]. However, we believe that, as indicated by Kalis et al. [ 90 ] on the pages of Harvard Business Review, the management of costly back-office problems should also be addressed.

The potential of algorithms includes data analysis. There is an immense quantity of data accessible now, which carries the possibility of providing information about a wide variety of medical and healthcare activities [ 91 ]. With the advent of modern computational methods, computer learning and AI techniques, there are numerous possibilities [ 79 , 83 , 84 ]. For example, AI makes it easier to turn data into concrete and actionable observations to improve decision-making, deliver high-quality patient treatment, adapt to real-time emergencies, and save more lives on the clinical front. In addition, AI makes it easier to leverage capital to develop systems and facilities and reduce expenses at the organisational level [ 78 ]. Studying contributions to the topic, we noticed that data accuracy was included in the debate, indicating that a high standard of data will benefit decision-making practitioners [ 38 , 77 ]. AI techniques are an essential instrument for studying data and the extraction of medical insight, and they may assist medical researchers in their practices. Using computational tools, healthcare stakeholders may leverage the power of data not only to evaluate past data ( descriptive analytics ) but also to forecast potential outcomes ( predictive analytics ) and to define the best actions for the present scenario ( prescriptive analytics ) [ 78 ]. The current abundance of evidence makes it easier to provide a broad view of patient health; doctors should have access to the correct details at the right time and location to provide the proper treatment [ 92 ].

Will medical technology de-skill doctors?

Further reflection concerns the skills of doctors. Studies have shown that healthcare personnel are progressively being exposed to technology for different purposes, such as collecting patient records or diagnosis [ 71 ]. This is demonstrated by the keywords (Fig.  6 ) that focus on technology and the role of decision-making with new innovative tools. In addition, the discussion expands with Lu [ 93 ], which indicates that the excessive use of technology could hinder doctors’ skills and clinical procedures' expansion. Among the main issues arising from the literature is the possible de-skilling of healthcare staff due to reduced autonomy in decision-making concerning patients [ 94 ]. Therefore, the challenges and discussion we uncovered in Fig.  11 are expanded by also considering the ethical implications of technology and the role of skills.

Implications

Our analysis also has multiple theoretical and practical implications.

In terms of theoretical contribution, this paper extends the previous results of Connelly et al., dos Santos et al, Hao et al., Huang et al., Liao et al. and Tran et al. [ 2 , 19 , 20 , 21 , 22 , 24 ] in considering AI in terms of clinical decision-making and data management quality.

In terms of practical implications, this paper aims to create a fruitful discussion with healthcare professionals and administrative staff on how AI can be at their service to increase work quality. Furthermore, this investigation offers a broad comprehension of bibliometric variables of AI techniques in healthcare. It can contribute to advancing scientific research in this field.

Limitations

Like any other, our study has some limitations that could be addressed by more in-depth future studies. For example, using only one research database, such as Scopus, could be limiting. Further analysis could also investigate the PubMed, IEEE, and Web of Science databases individually and holistically, especially the health parts. Then, the use of search terms such as "Artificial Intelligence" OR "AI" AND "Healthcare" could be too general and exclude interesting studies. Moreover, although we analysed 288 peer-reviewed scientific papers, because the new research topic is new, the analysis of conference papers could return interesting results for future researchers. Additionally, as this is a young research area, the analysis will be subject to recurrent obsolescence as multiple new research investigations are published. Finally, although bibliometric analysis has limited the subjectivity of the analysis [ 15 ], the verification of recurring themes could lead to different results by indicating areas of significant interest not listed here.

Future research avenues

Concerning future research perspectives, researchers believe that an analysis of the overall amount that a healthcare organisation should pay for AI technologies could be helpful. If these technologies are essential for health services management and patient treatment, governments should invest and contribute to healthcare organisations' modernisation. New investment funds could be made available in the healthcare world, as in the European case with the Next Generation EU programme or national investment programmes [ 95 ]. Additionally, this should happen especially in the poorest countries around the world, where there is a lack of infrastructure and services related to health and medicine [ 96 ]. On the other hand, it might be interesting to evaluate additional profits generated by healthcare organisations with AI technologies compared to those that do not use such technologies.

Further analysis could also identify why some parts of the world have not conducted studies in this area. It would be helpful to carry out a comparative analysis between countries active in this research field and countries that are not currently involved. It would make it possible to identify variables affecting AI technologies' presence or absence in healthcare organisations. The results of collaboration between countries also present future researchers with the challenge of greater exchanges between researchers and professionals. Therefore, further research could investigate the difference in vision between professionals and academics.

In the accounting, business, and management research area, there is currently a lack of quantitative analysis of the costs and profits generated by healthcare organisations that use AI technologies. Therefore, research in this direction could further increase our understanding of the topic and the number of healthcare organisations that can access technologies based on AI. Finally, as suggested in the discussion section, more interdisciplinary studies are needed to strengthen AI links with data quality management and AI and ethics considerations in healthcare.

In pursuing the philosophy of Massaro et al.’s [ 11 ] methodological article, we have climbed on the shoulders of giants, hoping to provide a bird's-eye view of the AI literature in healthcare. We performed this study with a bibliometric analysis aimed at discovering authors, countries of publication and collaboration, and keywords and themes. We found a fast-growing, multi-disciplinary stream of research that is attracting an increasing number of authors.

The research, therefore, adopts a quantitative approach to the analysis of bibliometric variables and a qualitative approach to the study of recurring keywords, which has allowed us to demonstrate strands of literature that are not purely positive. There are currently some limitations that will affect future research potential, especially in ethics, data governance and the competencies of the health workforce.

Availability of data and materials

All the data are retrieved from public scientific platforms.

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Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

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Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Since Neolithic humans fashioned the first scalpel out of stone, new machines and methods have changed the way we practice medicine and learn about the human body. Physicians moved on from those early scalpels to stethoscopes, X-rays, and MRIs, the better to understand the workings of the human body. With these new understandings has come translational research that transfers findings from the lab into new, more effective treatments and medicines. Dean Robert J. Alpern, M.D., Ensign Professor of Medicine, discussed basic science and advances in clinical care; technology and patient care; and the role of serendipity in research with Yale Medicine .

What have been some of the key inventions or discoveries that have advanced clinical care and medical research? In the past 50 to 100 years, there have been so many advances that it’s hard to rank any one above the other. Obviously, some come to mind—the discovery of the structure of DNA, recombinant DNA, electron microscopy, knockout technology. The new gene editing technology, CRISPR, is really going to transform research. It’s important to point out that the major advances in health care have been based on basic scientific findings. DNA technology and the structure of DNA were basic science findings that now drive clinical genetics. The understanding of how cells grow has transformed cancer care. Basic understandings of the immune system have led to immunotherapy for cancer.

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Radiologic Technology Clinical Manual

A framework for predicting radiologic physics achievement among radiologic technology students.

Radiologic Physics is one of the most challenging professional subjects in the Radiologic Technology (RT) field. It encompasses a wide range of physics concept, calculations, and real-life imaging practices. As observed, most of the students failed in this subject, leading to students dropping out early in the course. To circumvent this daunting issue, a framework for predicting the subject’s achievement should be developed using various tenets of learning strategies and management. This study aims to explore a framework that can predict Radiologic Physics achievement among RT students. Subjects were 954 Radiologic Physics students (480 males and 474 females) randomly selected from 12 Radiologic Technology schools in the Philippines. Their ages ranged from 18 to 22 years (mean age 19.5, SD 2.4). Seven instruments were used to collect data for the study: Physics Learning Strategies Scale, Inventory of Students Attitude Towards Radiologic Physics; Class Involvement Scale; Teacher-Directed Activities Scale; Parental Influence towards Academic Success Scale, and Radiologic Physics Achievement Test. Path analysis was utilized to identify the best fitting framework. The best fitting framework explained 92% of Radiologic Physics achievement variance. Cognitive and metacognitive learning strategies, attitudes towards Radiologic Physics, class involvement, teacher-directed activities, and parental influence exerted a positive effect on Radiologic Physics achievement. Teacher-directed activities, parental influence, and attitudes towards the subject had a positive impact towards cognitive learning strategies. Moreover, teacher-directed activities and parental influence exerted a positive effect on class involvement while parental influence had a positive impact on metacognitive learning strategies. Teacher-direct activities, attitudes towards the subject, and parental influence contributed indirectly to achievement via cognitive learning strategies. Teacher-directed activities contributed indirectly to achievement via class involvement. Finally, parental influence contributed indirectly to achievement via metacognitive learning strategies and class involvement. A framework for predicting Radiologic Physics achievement among RT students could be used to understand the performance of students and innovate learning strategies in the RT education.

Structural Models of Self-Efficacy of Filipino Radiologic Technology Educators, Current Learners, and Prospective Students in the Senior High School

This study aims to explore three different structural models of self-efficacy for Filipino Radiologic Technology educators, current learners, and prospective students in the senior high school. Subjects were 256 Radiologic Technology educators (102 males and 154 females) and 2,451 Radiologic Technology students (1,525 males and 926 females), randomly selected from 22 Radiologic Technology schools in the Philippines. A total of 4,263 prospective Radiologic Technology students from the 30 senior high schools in the Philippines were also sampled as respondents. Six instruments were used to measure help-seeking, self-esteem, social support, motivation, self-regulation, and self-efficacy. Path analysis was utilized to identify the best fitting model. The best fit models of self-efficacy for Filipino Radiologic Technology educators and learners showed that help-seeking, self-esteem, social support, motivation, and self-regulation positively influenced self-efficacy, with social support exerting the greatest causal effect. The best fit model of self-efficacy for prospective Radiologic Technology students from senior high school reported that social support completely mediates the effects of help-seeking, self-esteem, motivation, and self-regulation on self-efficacy. The study highlights different results that could be arrived depending on whether future researchers decide to use the self-efficacy models for Filipino Radiologic Technology educators, current learners, and prospective students in the senior high school. The provision of highly supportive environment for Radiologic Technology educators, current learners, and prospective students in the senior high school is essential to increase self-efficacy, thereby improving their capabilities to hurdle the challenges in the academic milieu.

National Radiologic Technology Licensure Examination Performance: Predicting Success using Discriminant Analysis

This study was designed to identify variables that might be used as predictors for success on the national Radiologic Technology licensure examination. The census sample consisted of 2,036 graduates of a baccalaureate Radiologic Technology program in 2016, 2017, and 2018 from 24 higher education institutions (HEIs) in the Philippines. The investigators examined 12 variables to determine their predictive value for the national Radiologic Technology licensure examination success. Grades in all year levels of Radiologic Technology course were the four best predictors. Results of the discriminant analysis identified seven significant predictor variables leading to successful classification of 99.9 percent of all the passing graduates and 99.8 percent of the failing graduates in the national Radiologic Technology licensure examination. The use of this discriminant function to identify high-risk students has the advantage of early identification of failing. The large amount of 92 percent variance in the national RT licensure examination accounted for in this study may substantiate the claim of high accuracy of the discriminant function used. This is the first study to discriminate passing from failing graduates in the national RT licensure examination based on the selected predictor variables and the astounding precision of classifying graduates is a remarkable result for HEIs included in the analysis.

Adjustment to college and academic performance: Insights from Filipino college freshmen in an allied health science course

This paper aims to know the relationship between the level of adjustment to college and academic performance of first year Radiologic Technology students of a higher education institution in the Philippines. A descriptive-correlational study using survey questionnaire was employed to 132 respondents who were chosen through stratified random sampling and Slovin’s formula. Standard questionnaires were used to gather data on the demographic profile and level of adjustment of the respondents while the academic performance was measured through the Weighted Point Average (WPA) requested from the school’s Registrar. Results showed that the majority of the respondents are female (53.8%), belong to middle income class (34.8%), were from STEM (59.1%) and travel between one kilometer and 10 kilometers to school (34.1%). The study reported a moderate level of adjustment and a 2.63 overall WPA of students. Test of difference showed that there is significant difference in the academic adjustment and academic strand taken during SHS (p<0.05); and in the institutional attachment and proximity of house to school (p<0.05). Bivariate correlation among variables revealed that there is no significant relationship between the level of adjustment to college and academic performance of first year Radiologic Technology students College (p>0.05).

Interprofessional Education Perceptions of Dental Assisting and Radiologic Technology Students Following a Live Patient Experience

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Biotechnology is a broad discipline in which biological processes, organisms, cells or cellular components are exploited to develop new technologies. New tools and products developed by biotechnologists are useful in research, agriculture, industry and the clinic.

Acetyl-CoA-independent malonyl-CoA biosynthesis

Malonyl-CoA is one of the fundamental building blocks for the synthesis of industrially or pharmaceutically important chemicals, but its biosynthesis via the innate acetyl-CoA carboxylation pathway remains slow and inefficient. Now, an artificial non-carboxylative malonyl-CoA biosynthetic pathway has been developed, significantly enhancing malonyl-CoA supply by boosting carbon and energy efficiency while sidestepping the inhibitions by host cell regulations.

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Researchers detect a new molecule in space

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New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team's open-access paper, “ Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I ,” appears in April 12 issue of The Astrophysical Journal Letters .

Zachary T.P. Fried , a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of 'methoxy' molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system , many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”

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MIT researchers have “discovered hitherto unknown space molecule while investigating a relatively nearby region of intense star birth,” reports Robert Lea for Space.com . This discovery “revealed the presence of a complex molecule known as 2-methoxyethanol, which had never been seen before in the natural world, though its properties had been simulated in labs on Earth,” writes Lea.

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Simulation makes the grade for teacher screening

by Murdoch University

New research has found that simulations are an effective on-entry screening tool for teaching candidates, exposing university students to authentic classroom dynamics, increasing their confidence, and providing a safe learning environment. The study is published in the Journal of Education for Teaching .

Australian universities are now required to implement non-academic on-entry evaluations for all teacher education candidates.

To meet this requirement, universities have introduced a variety of assessment methods, including interviews, written applications, psychometric tests, and most recently, simulation .

Murdoch University was the first university in Australia to implement teaching simulations and augmented learning environments into the general course structure, using Mursion technology (SimLab).

Murdoch University Dean and Head of the School of Education, Associate Professor Peter Whipp said his recent research sought to understand how useful these simulation tools could be in the initial screening of teaching students.

"The implications of our findings are significant for policy and practice," Professor Whipp said.

"Our research showed that simulation can reliably evaluate students' teaching dispositions, as well as provide insights into their motivations, planning, communication skills and how we can best support them.

"Students themselves also said they perceived simulation as an effective means to evaluate their on-entry performance, reporting that the experience was valuable and that it helped with their confidence, so they felt more prepared for the real classroom.

"The on-entry evaluations are important, so that we can help our students be the best they can be as a teacher in our education system once they graduate.

"The simulation process is efficient and effective. We are pleased to be a leader in this space and encourage other higher education institutes to embrace the technology."

The next steps in this research program include exploring the predictive nature of data captured at course entry and how this translates over time and experience.

The study results also emphasized the need to continue to expand the evidence base around this process.

Provided by Murdoch University

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Uptick in New York transit assault rate during pandemic has not returned to pre-pandemic levels despite safety plan

by Columbia University's Mailman School of Public Health

Has the New York City subway become less safe? This is the question Columbia University Mailman School of Public Health researchers sought to answer in their newest paper investigating rates of complaints to and arrests by the New York City Police Department Transit Bureau. The findings showed that anxieties related to crime on New York City transit rose following NYC's COVID-19 pandemic state of emergency declaration in 2020, leading to declines in subway ridership.

The results are published in the journal Injury Epidemiology .

The researchers were motivated by news reports indicating heightened concerns for safety when using public transit in New York City following the start of the COVID-19 pandemic. They aimed to quantify the effect of the COVID-19 pandemic on rates of crime within the subway system and to assess whether any changes were then influenced by the Subway Safety Plan, a program implemented by Mayor Eric Adams in February 2022 with stated aims of addressing concerns about safety as well as supporting those experiencing homelessness and serious mental illness.

Using publicly available data from the City and State of New York for the years 2018 to 2023, the researchers estimated changes in the rates of complaints to and arrests by the NYPD Transit Bureau per 1,000,000 subway riders per month. They also examined changes by the most common complaint and arrest types (assault, harassment, grand larceny, criminal mischief, and theft of services), as well changes in the proportion of arrests by demographic and geographic groups.

"Public transportation has so many public health benefits, including reductions in traffic crashes and air pollution, and improvements in physical health ," said Leah Roberts, data analyst in the Department of Epidemiology, at Columbia Mailman School, and first author. "Not feeling safe when riding the subway is a major threat to equity in transit access. We hoped to understand how safety might have changed with the COVID-19 pandemic and the Subway Safety Plan."

The results showed that following the COVID-19 pandemic declaration, there was an 84% increase in the rate of complaints to the NYPD Transit Bureau per rider, including a 99% increase in the rate of complaints for assault and a 125% increase in the rate of complaints for harassment, aligning with news media reports of an uptick in subway-related crime.

Following the Subway Safety Plan's enactment, the authors did not find evidence of decreases in complaint rates, though there was a small increase in the rate of arrests for harassment. This finding might suggest that the Subway Safety Plan's increased police presence has led to stronger enforcement of transit policies, though it is unclear why this effect was specific to harassment arrests.

"It's important to note that the subway remains a relatively low-crime area. Throughout our study period, over 100,000 subway rides occurred on average for every one complaint reported to the NYPD Transit Bureau," noted Christopher Morrison, Ph.D., assistant professor of Epidemiology at Columbia Mailman, and senior author. "But there have been significant increases in rates of complaints to police by subway riders, that haven't returned to pre-pandemic levels."

Another notable feature of the authors' findings was a decrease in the proportion of arrests for individuals racialized as white and the proportion of arrests in the borough of Manhattan following the Subway Safety Plan's enactment. Because there was not an absolute decrease in arrest rates during this period, this shift may suggest changes in the geographic distribution of transit policing throughout the city, rather than reductions in policing within Manhattan.

"We'd like to better understand the changes we're seeing in the demographic and geographic composition of individuals arrested. We know that recent statements by the MTA and law enforcement have indicated that they're pursuing an approach of strong fare enforcement, which they hope will lower rates of more serious crime in the subway system," stated Morrison, who is also affiliated with the School of Public Health and Preventive Medicine at Monash University, Australia.

"Though it may take more time for this strategy to have the intended effect, our analysis didn't find measurable decreases in rates of any complaint or arrest type. We also need to consider potential unintended consequences of this strategy, since we know a larger police presence and stronger enforcement doesn't make all subway riders feel safer. Other approaches to improving safety on transit, such as wait time reductions and improvements to station cleanliness could have better impacts for public health and public safety," Morrison added.

Co-authors are Christina Mehranbod, Brady Bushover, Ariana Gobaud, Evan Eschliman, Carolyn Fish, Siddhesh Zadey, and Xiang Gao, Columbia Mailman School of Public Health.

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