Artificial Intelligence Education for Health Professions Students: A Scoping Review

Artificial Intelligence Education for Health Professions Students: A Scoping Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Artificial Intelligence Education for Health Professions Students: A Scoping Review Fiona Buckmaster, Diane van Staden, Lauren Coetzee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6972197/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The rapid pace at which artificial intelligence (AI) technologies are being integrated into healthcare demands competency on the part of health professionals in how to effectively integrate these tools into their practice. However, not many universities currently teach health professions students (HPS) about AI. A scoping review was undertaken to map key themes and identify gaps in the available literature on how best to teach HPS about AI. Methods: This scoping review followed the PRISMA-ScR checklist and the Arksey and O’Malley five-stage framework. The aim was to discover what AI topics have been taught to HPS and what educational methods have been employed to teach HPS about AI. A search of 4 databases (PubMed, Scopus, CINAHL, ERIC) identified 10,979 unique titles which underwent a two-step screening process and 15 full text studies were included. Data were extracted in an iterative process. A narrative review approach was used to generating themes and reporting results. Results: Most of the included studies taught medical students about AI, although students from other health specialties such as nursing, pharmacy and dentistry also appeared in the literature. A broad range of topics about AI were delivered by the educational interventions which were synthesised using a modified framework from McCoy et al. (2020). The most frequent topics taught were foundational AI literacy and applying AI to healthcare practice. A wide variety of teaching methods were utilised, most commonly reading and lectures. Conclusions: Whilst some university programs are already implementing AI educational interventions for their health professions students, there remains a lack of consensus on what and how to teach about AI to HPS. Further research should be conducted to build an evidence base for the design, implementation and evaluation of AI curricula for HPS, particularly in teaching students from a wider range of health disciplines. Artificial Intelligence and Machine Learning Medical Informatics Artificial intelligence machine learning medical education healthcare education Figures Figure 1 Figure 2 Introduction Artificial intelligence (AI) has potential applications in almost every aspect of contemporary society ( 1 ). Within healthcare, there are current and potential future uses for AI in many different specialties including radiology ( 2 ), cardiology ( 3 ), dentistry ( 4 ), nursing ( 5 ) and pharmaceutical drug discovery ( 6 ). There is an expectation from the general public that the health sector will embrace this innovation in order to provide digitally-powered 21st century healthcare to patients ( 7 ). However, there are significant challenges that must be overcome when attempting to realise this potential and integrate AI into real-world healthcare practice. One such challenge is ensuring that the healthcare workforce has the appropriate knowledge and skills required to use AI tools safely and effectively in their practice. Integration of digital literacy into the education of health professions students (HPS) has been called for on a national level in several countries including the United States ( 8 ), United Kingdom ( 9 ) and Canada ( 10 ). However, despite students expressing an eagerness to learn about AI, few universities provide their HPS with training in this subject ( 11 – 13 ). Some attempt has been made to identify what and how to teach AI in medical education ( 14 – 16 ). However, there is no clear consensus on which competencies and educational methods should be used to teach students to become AI proficient and there is much in this area that still remains uncharted. In addition, there is little understanding of how best to teach AI to non-medical health professions students such as those in dentistry, optometry, or pharmacy. Furthermore, the relative novelty of this subject area means that the rate of publication about AI in healthcare is increasing rapidly. Therefore, there is a clear need to consolidate the most recent evidence available in this subject area, as previous synthesis of the literature may quickly become out of date. Considering this landscape, a scoping review was undertaken to map key themes and identify gaps in the available literature on how best to teach and prepare HPS about AI for the digitally-powered healthcare practice of the near future. Methods This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist ( 17 , 18 ) and the Arksey and O’Malley five-stage framework for scoping reviews ( 19 ). Stage 1: Identifying the Research Question What topics related to AI have been taught to HPS in universities? What educational methods have been employed in universities to teach HPS about AI? Stage 2: Identifying Relevant Studies Search strategies for PubMed/Medline, Scopus, CINAHL and ERIC were developed by the authors with the assistance of a health and human sciences academic librarian. Appropriate subject headings, keywords and free text terms relating to AI education for HPS were used in combination with Boolean operators to achieve optimal results. No restrictions on date or language were applied during the searches. For complete search strategies used, see Appendix 1. The population concept context (PCC) framework was employed as a guide for developing the search strategies (Table 1 ). Table 1 PCC Framework. Population Students in health professions, including (but not limited to) medicine, nursing, dentistry, pharmacy, biomedical science, allied health professions, optometry, radiography. Concept Teaching health professions students about artificial intelligence, either generally or AI as a medical device (AIaMD). Context University setting. The searches were run and results downloaded on 10th July 2024. Additional relevant sources were identified using citation pearl growing. Results of the search strategy were uploaded to Covidence ( 20 ). Stage 3: Study Selection A two-step screening process was conducted which consisted of, firstly, title and abstract screening and, thereafter, full text review. For a study to be included, the following eligibility criteria was required to be met: Discussed an educational intervention which had actually been implemented. HPS took part in the educational intervention. The educational intervention was about AI, either generally or AIaMD. The educational intervention took place in a university setting. The exclusion criteria encompassed studies that: Taught fully-qualified health professionals and not HPS. Taught non-health professions students. Took place outside of a university setting. Proposed a training course or educational intervention but did not actually implement it. Taught digital health competencies which did not specifically include AI (e.g. robotics). Used AI to teach health professions students (e.g. virtual simulations, virtual tutors) without teaching any aspect of how the AI tool itself worked. More than 10 years old. Full text was not available. Did not have a full text in English. Using Covidence, duplicate records were eliminated ( 20 ). The first step of the screening process (title and abstract screening) was conducted by a single reviewer (FB). Following this, the second step of the screening process (full text review) was initially conducted by the first reviewer (FB). During the full text review, any studies where the eligibility was unclear or questionable were marked as “Maybe” and were reassessed by a second reviewer (LC). Both reviewers then discussed these studies to reach a consensus. Stage 4: Charting the Data In order to answer the two research questions, an iterative process was used to create a charting form to extract data from the included studies. Data was extracted in the following domains: article details, study details, educational intervention details, implementation factors (Table 2 ). Inductive coding was used to code study details, educational intervention details and implementation factors. The charting form was initially piloted on five studies to ensure that all data relevant to the research questions were extracted. Table 2 Domains and subdomains for data extraction. Domain Subdomain Article details Study type, year and location. Study details Population, institution, intervention evaluation method. Educational intervention details Name, duration, mode of delivery, pedagogical theories, educational methods, topics included, student evaluation methods. Implementation factors Facilitators and barriers. As the review progressed, if the reviewers wished to extract additional information of relevance, further revisions to the charting form could be made iteratively as required. Stage 5: Collating, Summarising and Reporting the Results A narrative review approach was used to collating, summarising and reporting the results ( 21 ). A numerical analysis using descriptive statistics was used to report on each domain. Tables and charts were produced in order to map the geographic distribution and chart the key characteristics of the studies, and a thematic analysis was conducted. Curriculum topics were coded and then grouped using a modified version of McCoy et al. ( 22 )’s domains regarding what physicians need to understand about AI. As this scoping review was following the recommendations by Arksey and O’Malley ( 19 ), it was not deemed necessary to perform a systematic quality appraisal of the studies included. However, whilst a quality appraisal was not undertaken, the reviewers ensured that all included studies declared either approval or an appropriate ethics exemption granted by an institutional review board or ethics committee. Results Study Characteristics Our search identified 10,979 unique titles, of which 15 studies were included in the final analysis ( 23 – 37 ). Most of the included studies originated from either the United States (n = 6, 40.0%) or Germany (n = 4, 26.7%), although there was generally a diverse geographic spread which included Belgium (n = 1, 6.7%), Finland (n = 1, 6.7%), Puerto Rico (n = 1, 6.7%), Turkey (n = 1, 6.7%), and the United Kingdom (n = 1, 6.7%). Despite the selection criteria allowing the inclusion of studies published within the last 10 years, all included studies had been published since the year 2020, with most of the included studies published in either 2023 (n = 5, 33.3%) or 2024 (n = 6, 40.0%) (Fig. 2 ). Table 3 Publication and educational intervention titles of the included studies. Title (Author, Year) University Name of Educational Intervention AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study. (Abid et al., 2024) ( 36 ) Emory University School of Medicine Name not reported An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum. (Culp et al., 2024) ( 35 ) University of Southern California Name not reported AI for Doctors - A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging. (Hedderich et al., 2021) ( 34 ) Technical University of Munich AI for Doctors: Medical Imaging Assessing the Impact of AI Education on Hispanic Healthcare Professionals’ Perceptions and Knowledge. (Heredia-Negrón et al., 2024) ( 33 ) University of Puerto Rico Artificial Intelligence and Machine Learning Applied to Health Disparities Research (AIML + HDR) Grounded in reality: artificial intelligence in medical education. (Krive et al., 2023) ( 37 ) University of Illinois at Chicago Analytics and Artificial Intelligence in Medicine (A2IM) Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis. (Kröplin et al., 2024) ( 32 ) University of Rostock Digital Health - Digitalisation and Digital Transformation of Medicine Propagating AI Knowledge Across University Disciplines- The Design of A Multidisciplinary AI Study Module. (Laato et al., 2020) ( 31 ) University of Turku AI in Diagnostics, Pharmaceutics and Imaging (Biomedicine); AI in Nursing Sciences (Nursing) Artificial intelligence in medical education and the meaning of interaction with natural intelligence - an interdisciplinary approach. (Lang & Repp, 2020) ( 30 ) Justus-Liebig-University Gießen (Natural) Science and Technology in Medicine – SciTecMed Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: a single group pre-and post-test study. (Laupichler et al., 2022) ( 29 ) Bonn Medical School KI-LAURA (Artificial intelligence in the teaching of ophthalmology and radiology) Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system. (Luo et al., 2024) ( 24 ) Northwestern University Name not reported A Pilot Remote Curriculum to Enhance Resident and Medical Student Understanding of Machine Learning in Healthcare. (Meade et al., 2023) ( 28 ) Case Western Reserve University School of Medicine Machine Learning in Healthcare Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study. (Park et al., 2024) ( 27 ) Geisel School of Medicine at Dartmouth Digital Health Scholars (DHS) Integrating Artificial Intelligence into Medical Education: Lessons Learned From a Belgian Initiative. (Pizzolla et al., 2023) ( 26 ) University of Mons AI and Digital Medicine Effect of Artificial Intelligence Course in Nursing on Students' Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study. (Taskiran, 2023) ( 25 ) Aydin Adnan Menderes University Name not reported Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. (Van De Venter et al., 2023) ( 23 ) City University London Introduction to Artificial Intelligence for Radiographers Following analysis of the descriptive elements, a deeper contextualisation of the studies led to the derivation of a number of themes. Which healthcare specialties are being taught about AI? Most of the included studies taught medical students about AI ( 24 , 26 – 30 , 32 , 34 , 36 , 37 ). Other health professions students taught about AI were in nursing ( 25 , 31 ), biomedicine ( 31 ), medical biotechnology ( 32 ), pharmacy ( 35 ), dentistry ( 32 ), radiography ( 23 ) or were non-specified health-related students ( 33 ). Only two of the included studies taught students from more than one health discipline ( 31 , 32 ). Some of the included studies taught qualified health professionals or taught student populations from other non-health disciplines in addition to HPS ( 23 , 24 , 28 , 30 , 31 , 33 , 34 ). For the purposes of this scoping review, only the information about university-level HPS was extracted and synthesised from these studies. The number of students who completed the educational intervention varied across the included studies. The most common student population size was around 20 students ( 23 , 26 , 29 , 32 , 33 , 36 , 37 ). However, student population sizes as low as three ( 28 ) and as high as 170 ( 25 , 35 ) were reported (Appendix 2). What are health professions students being taught about AI? The included studies delivered educational interventions which covered a broad range of topics about AI. The initial intention of this study was to frame topics based on the three domains from McCoy et al. ( 22 ) regarding what clinicians need to understand about AI in a clinical context: how to use it, interpret it, and explain it. Of these AI topics, 10 fell under the use it domain, three fell under the interpret it domain, and one fell under the explain it domain. However, the three-domain framework by McCoy et al. ( 22 ) was not fully inclusive of all the thematic elements that arose from the literature. Four topics did not fit into the use it, interpret it, explain it categorisation, and so an additional innovate it domain was created (Table 4 ). The most common topics covered were foundational AI literacy, which covered basic AI concepts and terminologies ( 23 , 25 – 31 , 33 – 37 ), and integrating and applying AI to healthcare practice ( 23 , 25 – 32 , 34 – 37 ). Ten studies had educational interventions which included how AI learns or is trained as a topic ( 23 , 25 – 27 , 29 , 30 , 33 – 36 ). Programming ( 27 , 28 , 30 , 33 , 34 , 36 ), the ethical issues surrounding the use of AI ( 23 , 25 , 30 , 32 , 34 , 36 ), and AI in medical imaging ( 23 , 26 , 29 – 31 , 34 ) were included in six studies each. The full range of topics covered by the educational interventions in the included studies can be seen in Table 4 . Table 4 Topics covered in the educational interventions of the included studies framed by a modified version of McCoy et al. ( 22 )’s domains. Characteristic No. (%) of studies (n = 15) Studies Use it Foundational AI literacy 13 (86.7) 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37 Integrating and applying AI to healthcare practice 13 (86.7) 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37 Ethics and AI 6 (40.0) 23, 25, 30, 32, 34, 36 AI in medical imaging 6 (40.0) 23, 26, 29, 30, 31, 34 AI in clinical decision support / diagnostics 4 (26.7) 26, 29, 31, 37 Law / regulation / governance and AI 3 (20.0) 23, 25, 34 Impact of AI on medicine and/or nursing 2 (13.3) 25, 36 Predictive analytics 1 (6.7) 37 Data protection and information security 1 (6.7) 32 Generative AI 1 (6.7) 32 Interpret it How AI learns / is trained 10 (66.7) 23, 25, 26, 27, 29, 30, 33, 34, 35, 36 Limitations / pitfalls of AI 4 (26.7) 27, 29, 30, 36 AI in health disparities / inequalities 2 (13.3) 28, 33 Explain it Understanding and interpreting AI research 3 (20.0) 27, 28, 34 Innovate it Programming 6 (40.0) 27, 28, 30, 33, 34, 36 Bioinformatics 1 (6.7) 33 Networking with AI industry 1 (6.7) 23 Other digital health competencies (e.g. virtual reality, robotics, telemedicine) 2 (13.3) 25, 32 Not specified Not specified 1 (6.7) 24 Most studies published a partial curriculum from their educational intervention ( 23 , 26 , 27 , 29 , 30 , 32 – 35 , 37 ). Two studies published a full curriculum ( 28 , 36 ), and three studies did not publish any curriculum ( 24 , 25 , 31 ). How are health professions students being taught about AI? The included studies had educational interventions which were delivered in a wide range of formats. Most of the studies delivered their educational intervention with some online format, either fully online ( 23 , 28 , 29 , 33 , 34 , 36 , 37 ) or in a hybrid format ( 26 , 27 ). The majority of educational interventions were delivered as an elective course or module ( 23 , 25 , 27 – 34 , 36 , 37 ). The most common duration was around three months ( 23 , 25 , 29 , 31 – 34 ), the approximate length of a single university semester. However, the duration varied from as short as two weeks ( 35 ) to as long as seven months ( 27 ). A wide variety of educational methods were used to teach the AI courses. Reading, such as scientific articles, textbooks or websites, was the most common educational method employed ( 23 , 26 – 29 , 33 – 37 ). Lectures, either live ( 23 , 25 , 27 – 29 , 32 , 34 , 35 , 37 ) or pre-recorded ( 23 , 26 , 28 , 33 , 34 ), were another educational method which was frequently used. By contrast, educational methods such as seminars ( 30 ) and simulated case scenarios ( 37 ) were used by relatively few studies, although this may be due to a lack of consistency in naming educational methods used between the studies, as it is unclear how seminars may differ from other methods used by other studies such as tutorials ( 23 , 24 ). The full range of educational methods in the included studies can be seen in Table 5 . Only one study created an educational intervention solely using pre-existing educational resources and did not develop any new learning materials for the course ( 36 ). Table 5 Characteristics of the educational interventions in the included studies. Characteristic No. (%) of studies (n = 15) Studies In-Person or Online Online 7 (46.7) 23, 28, 29, 33, 34, 36, 37 In-person 3 (20.0) 25, 32, 35 Hybrid 2 (13.3) 26, 27 Not specified 3 (20.0) 24, 30, 31 Elective or Mandatory Elective 12 (80.0) 23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37 Mandatory 3 (20.0) 24, 26, 35 Duration ~ 2 weeks 1 (6.7) 35 ~ 1 month 3 (20.0) 28, 36, 37 ~ 3 months 7 (46.7) 23, 25, 29, 31, 32, 33, 34 7 months 1 (6.7) 27 Not specified 3 (20.0) 24, 26, 30 Educational Methods Used Reading 10 (66.6) 23, 25, 27, 28, 29, 33, 34, 35, 36, 37 Live lectures 9 (60.0) 23, 25, 27, 28, 29, 32, 34, 35, 37 Group discussions 7 (46.7) 23, 24, 26, 27, 29, 30, 37 Online videos 6 (40.0) 23, 28, 29, 33, 36, 37 Pre-recorded lectures 5 (33.3) 23, 26, 28, 33, 34 Practical tasks 4 (26.7) 26, 27, 29, 35 Demonstrations 3 (20.0) 27, 29, 30 Open-book multiple choice questionnaire 3 (20.0) 26, 34, 37 Tutorials 2 (13.3) 23, 24 Pre-existing Massive Open Online Course (MOOC) 1 (6.7) 36 Simulated case scenarios 1 (6.7) 37 Seminars 1 (6.7) 30 Podcasts 1 (6.7) 23 Industry-led workshops 1 (6.7) 23 Not specified 1 (6.7) 31 New Learning Materials Created Yes 13 (86.7) 23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37 No 1 (6.7) 36 Not specified 1 (6.7) 24 Pedagogical Theories Applied Flipped classroom 3 (20.0) 23, 29, 34 Constructivism 1 (6.7) 37 Backwards design 1 (6.7) 37 Integrative learning 1 (6.7) 27 Not specified 10 (66.7) 24, 25, 26, 28, 30, 31, 32, 33, 35, 36 Student Evaluation Methods Used Individual project or term paper 5 (33.3) 23, 28, 29, 32, 36 Group project 4 (26.7) 26, 28, 35, 37 MCQs 3 (20.0) 26, 29, 37 Self-reflective exercise 1 (6.7) 37 None reported 7 (46.7) 24, 25, 27, 30, 31, 33, 34 Relatively few studies reported on the pedagogical theories applied to underpin their educational intervention. Flipped classroom was the most commonly reported pedagogical theory and was used by three of the included studies ( 23 , 29 , 34 ). Eight of the included studies reported on methods that were used to evaluate students’ performances in the AI courses. Individual assignments were used by five studies which included term papers, literature reviews, project proposals, and programming exercises ( 23 , 28 , 29 , 32 , 36 ). Group projects were employed by four studies which included creating videos about AI topics, oral presentations, and programming exercises ( 26 , 28 , 35 , 37 ). Multiple choice questionnaires were used by three studies ( 26 , 29 , 37 ). One study reported using weekly self-reflective exercises which were reviewed by faculty ( 37 ). How are AI courses for health professions students being evaluated? Most studies used non-validated subjective surveys to evaluate their educational intervention, either pre- and post-course ( 27 – 30 , 32 – 36 ) or post-course only ( 25 , 26 ) (Appendix 2). Only two studies ( 25 , 29 ) made use of Karaca et al. ( 38 )’s pre-existing validated Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). Qualitative analysis of the educational intervention was conducted by one study using semi-structured interviews ( 23 ). Many studies discussed facilitators and barriers to implementing their educational interventions (Appendix 2). The most common facilitator reported was a high level of interest about AI among HPS ( 27 – 29 , 31 , 32 , 34 ). The most commonly reported barrier was a lack of student time ( 27 , 28 , 30 , 34 , 37 ). This lack of time may have been due to a busy existing curricular schedule, concurrent examinations for other modules, or an underestimation of the time effort required to complete the course. All extracted data from the included studies have been categorised into themes, with no extracted data omitted from thematic grouping. Discussion This scoping review collated and synthesised the available evidence regarding teaching health professions students in university settings about artificial intelligence. A deeper understanding of what is being taught about AI and how AI is being taught to HPS was gained. However, the available literature in this field is still limited and significant gaps remain in understanding the best way to teach HPS about AI. Previous reviews in this area have focused on the integration of AI into medical education ( 14 – 16 ). Whilst medical students were the most common health discipline in this scoping review, present in 10 out of the 15 included studies, this review also included AI educational interventions taught to students in nursing, pharmacy, dentistry, radiography, medical biotechnology, biomedicine and non-specific health related students. The inclusion of a broader range of health specialties may make the results of this present study more generalisable across health disciplines. However, whilst this study did include a wider range of health professions than in previous reviews, many more health disciplines such as optometry, audiology, or occupational therapy are not represented in the existing literature to date. Unlike previous reviews on AI in health education, this scoping review did not include any perspective or commentary articles or proposed curricula, but only those which had actually implemented a manifest educational intervention about AI to HPS ( 14 , 16 , 39 , 40 ). Despite this distinction in the types of studies included, a similar lack of consensus on the optimal way to teach HPS about AI was found. This was illustrated by the broad range of topics about AI covered, educational methods employed and course durations in the included studies. Some of the topics which were frequently covered, such as foundational AI literacy (13 out of 15 studies) and applying AI to healthcare practice (13 out of 15 studies), align closely with the topics identified as highly important to include in medical AI education by Civaner et al.( 41 ) in their cross-sectional needs assessment. However, other highly important topics identified by this needs assessment, such as training to prevent and solve ethical problems that may arise with AI applications, were included by relatively few studies (6 out of 15 studies) in this scoping review. The topics covered by the educational interventions included in this scoping review were categorised based on a modified version of McCoy et al. ( 22 )’s domains. McCoy et al. ( 22 ) suggested that in order to understand how to deploy AI in a clinical context, physicians should learn how to use it, interpret it and explain it. Most of the topics covered by the educational interventions in this review were categorised under these three domains, however, four of the topics covered did not appear to fit under the use it , interpret it , explain it categorisation. These topics, which included programming and networking with AI industry, could be considered as not necessary for healthcare practitioners to know in order to apply AI tools to their everyday clinical practice, but may be useful to learn for clinicians who wish to drive innovation in AI. Therefore, an additional fourth domain of innovate it , was created for the categorisation of the AI topics in this review. These innovate it topics may be more well-suited to an extracurricular program targeted towards those HPS with a particular interest in AI who wish to be AI innovators. This approach has already been demonstrated by one of the included studies in this scoping review, where technical and non-technical tracks were created for the AI course and programming was only taught to students on the technical track ( 36 ). This approach does have some limitations, however, as Abid et al. ( 36 ) found that very few HPS possessed the requisite pre-existing knowledge needed to undertake the technical track of their AI course. Most of the included studies (12 out of 15 studies) made some effort to evaluate the effectiveness of their interventions and reported generally positive results in achieving their respective aims. The most common evaluation method was the use of pre- and post-intervention surveys (9 out of 15 studies). This is similar to the findings of Charow et al. ( 40 ) and Doherty et al. ( 42 ). Despite the widespread use of pre- and post-surveys, these were generally non-validated and measured a wide range of mostly subjective parameters. The lack of consistency between the evaluation methods of the included studies makes comparisons between these studies difficult. In order to achieve more objective comparison of educational intervention effectiveness between studies, standardised and validated instruments such as the MAIRS-MS should be utilised ( 38 ). The MAIRS-MS was utilised by 2 out of 15 included studies of this scoping review, although it was applied in different ways. In one study, the MAIRS-MS was used to compare students who had undergone an AI educational course to a control group who received no AI education ( 25 ), whereas in the other study, students took the MAIRS-MS twice and their results pre- and post-course were compared ( 29 ). Therefore, although a standardised instrument was used, due to the differences in application, a direct comparison between these two studies is still difficult. The MAIRS-MS instrument has some limitations, such as its original intended purpose to measure status quo in AI readiness at a single point in time rather than to compare between different points in time before and after an educational intervention. However, the more widespread use of a standardised instrument such as MAIRS-MS applied in a standardised manner would allow for more constructive comparisons to be made between studies and therefore more robust conclusions to be drawn on which educational interventions are most effective. Limitations The findings of this scoping review should be considered within the context of its limitations. Due to the large volume of studies generated by the search results, the first step of the screening process was completed only by a single reviewer and was not duplicated by a second reviewer. To account for this, the first reviewer used a low threshold for sending studies to full-text review. During the full-text review, any studies whose eligibility for inclusion was considered questionable or borderline were reviewed by a second reviewer, discussed, and a consensus between reviewers was reached. In addition, the search for this scoping review was performed on 10th July 2024, however the rate of publication in this area is increasing rapidly. There are likely to be further relevant studies which have been published in this area since the search date which have not been included in this review. Therefore, a repeat of this scoping review may be performed in future to provide an updated synthesis of the literature in this area. Furthermore, whilst the research team comprised of members with expertise in AI and health professions education, the sixth stage of the Arksey and O’Malley scoping review framework of external expert consultation was not performed, which could be viewed as a limitation. Conclusion Artificial intelligence is expected to revolutionise the healthcare landscape in the coming years and health professions students must be adequately prepared for this. It is imperative that universities providing health professions degrees keep pace with AI innovations and provide appropriate training in AI literacy for their students. This review of the available literature has found that whilst some university programs are already implementing AI educational interventions for their health professions students, there remains a severe lack of consensus on what and how to teach about AI to HPS. Future educational interventions should use an evidence-based approach in the design, implementation and evaluation of their curricula for the inclusion of AI into HPS training. Further research should be conducted to build this evidence base, particularly in teaching students from a wider range of health disciplines. 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World Neurosurg [Internet]. -12 [cited Dec 16, 2024];180:e142–8. Available from: https://pubmed.ncbi.nlm.nih.gov/37696433/ Laupichler MC, Hadizadeh DR, Wintergerst MWM, von der Emde L, Paech D, Dick EA, Raupach T Effect of a flipped classroom course to foster medical students’ AI literacy with a focus on medical imaging: A single group pre-and post-test study. BMC Medical Education [Internet]. 2022-11-18 [cited Dec 16, 2024];22(1):803. Available from: https://doi.org/10.1186/s12909-022-03866-x Lang J, Repp H (2020) Artificial intelligence in medical education and the meaning of interaction with natural intelligence - an interdisciplinary approach. GMS J Med Educ [Internet]. [cited Dec 16, 2024];37(6):Doc59. Available from: https://pubmed.ncbi.nlm.nih.gov/33225051/ Laato S, Vilppu H, Heimonen J, Hakkala A, Björne J, Farooq A, Salakoski T, Airola A, Propagating AI Knowledge Across University Disciplines- The Design of A Multidisciplinary AI Study Module. In: [Internet]; 20202020. pp. 1–9.cited Dec 16, 2024]. Available from: https://ieeexplore.ieee.org/document/9273940 10.1109/FIE44824.2020.9273940 Kröplin J, Maier L, Lenz J, Romeike B Knowledge transfer and networking upon implementation of a transdisciplinary digital health curriculum in a unique digital health training culture: Prospective analysis. JMIR Med Educ [Internet]. 2024-04-15 [cited Dec 16, 2024];10:e51389. Available from: https://pubmed.ncbi.nlm.nih.gov/38632710/ Heredia-Negrón F, Tosado-Rodríguez EL, Meléndez-Berrios J, Nieves B, Amaya-Ardila CP, Roche-Lima A (2024) Assessing the impact of AI education on hispanic healthcare professionals’ perceptions and knowledge. Education Sciences [Internet]. /4 [cited Dec 16, 2024];14(4):339. 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JMIR Medical Education [Internet]. 2024-02-20 [cited Dec 16, 2024];10(1):e46500. Available from: https://mededu.jmir.org//1/e46500 Krive J, Isola M, Chang L, Patel T, Anderson M, Sreedhar R (2023) Grounded in reality: Artificial intelligence in medical education. JAMIA Open [Internet]. June 1 [cited May 10, 2024];6(2):ooad037. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10234762/ Karaca O, Çalışkan SA, Demir K Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study. BMC Med Educ [Internet]. 2021-02-18 [cited Aug 4, 2024];21(1):112. Available from: https://pubmed.ncbi.nlm.nih.gov/33602196/ Weidener L, Fischer M (2023) Teaching AI ethics in medical education: A scoping review of current literature and practices. Perspect Med Educ [Internet] 12(1):399 Charow R, Jeyakumar T, Younus S, Dolatabadi E, Salhia M, Al-Mouaswas D, Anderson M, Balakumar S, Clare M, Dhalla A, Gillan C, Haghzare S, Jackson E, Lalani N, Mattson J, Peteanu W, Tripp T, Waldorf J, Williams S, Tavares W, Wiljer D (2021) Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review. JMIR Publications Inc.. 10.2196/31043 Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A (2022) Artificial intelligence in medical education: A cross-sectional needs assessment. BMC Medical Education [Internet]. [cited Jul 28, 2024];22:772. Available from: https://bmcmededuc.biomedcentral.com/articles/ 10.1186/s12909-022-03852-3 Doherty G, McLaughlin L, Hughes C, McConnell J, Bond R, McFadden S A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff. Radiography [Internet]. 2024-03-01;30(2):474–82. Available from: https://www.sciencedirect.com/science/article/pii/S1078817423002596 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix1.docx Supplementary Appendix 1 - Search Strategy Appendix2.docx Supplementary Appendix 2 - Additional Results Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6972197","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":476175313,"identity":"e77de51c-e4f3-4562-8cbc-2000f6933560","order_by":0,"name":"Fiona Buckmaster","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYBAC9mYwdYCBgQcqwi/BwCABZvFg18JzGEVLAgOD5AxCWg6gazG4QUgLO/OxBz8Y7iT295xO/Pjzh02+8e0ewxsMNXYMBmcOYNfCzJZu2MPwLHHG2d7N0jwJaZbb7pwxtmA4lsxgcLYBqxZ7Zh4zCR6Gw4kb+Hk3SDMkHDYwu5FjJsHAdoDB4DwOhwG1SP6BaNn880fCfwPjGSAt//BrkQbbwtu7TYIn4YCBgQRQC2PbAZwOA/olTVrG4JnxjDNnt1nzpCUbSNxIK7ZI7EvmkcTlff7DxyTfVNyR7e/J3Xzzh42dAf+M5I03Pnyzk+M7k4DdZWBggC6QgDNWRsEoGAWjYBQQAwAJW1mzBL5PTgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-6460-9961","institution":"University of Huddersfield","correspondingAuthor":true,"prefix":"","firstName":"Fiona","middleName":"","lastName":"Buckmaster","suffix":""},{"id":476175544,"identity":"98a6b752-03d3-4368-970b-2f4e28c22b99","order_by":1,"name":"Diane van Staden","email":"","orcid":"https://orcid.org/0000-0003-2028-1711","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Diane","middleName":"van","lastName":"Staden","suffix":""},{"id":476175545,"identity":"1a6a0591-42cc-41ec-b573-df5bc7a74fc4","order_by":2,"name":"Lauren Coetzee","email":"","orcid":"https://orcid.org/0000-0002-8083-9724","institution":"University of Huddersfield","correspondingAuthor":false,"prefix":"","firstName":"Lauren","middleName":"","lastName":"Coetzee","suffix":""}],"badges":[],"createdAt":"2025-06-25 08:17:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6972197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6972197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85453877,"identity":"fed8e979-acda-43a8-9aa0-febc073004b5","added_by":"auto","created_at":"2025-06-26 05:47:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlow diagram of the search and selection process, adapted from PRISMA.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6972197/v1/186b4428bef9d3ed52768ff2.png"},{"id":85453625,"identity":"18013f91-3f57-46b0-be85-b2bd0d6b48b9","added_by":"auto","created_at":"2025-06-26 05:39:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23473,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePublication year of included studies.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6972197/v1/6106583a0d5ad06233f2fddc.png"},{"id":85454466,"identity":"8bae41d2-4af5-4486-9a39-d18a2ec34690","added_by":"auto","created_at":"2025-06-26 06:03:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1365470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6972197/v1/1e4818f7-c6bd-419e-8025-76c5e7f87712.pdf"},{"id":85453876,"identity":"03c73960-7e15-401a-9289-eda152f02279","added_by":"auto","created_at":"2025-06-26 05:47:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19739,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Appendix 1 - Search Strategy\u003c/p\u003e","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6972197/v1/be97baba81047b1915f985e3.docx"},{"id":85453623,"identity":"7330a8e9-b0ce-4c0c-a0cd-56704c682a5e","added_by":"auto","created_at":"2025-06-26 05:39:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29628,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Appendix 2 - Additional Results\u003c/p\u003e","description":"","filename":"Appendix2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6972197/v1/101155b48ad8d2e4b9930407.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eArtificial Intelligence Education for Health Professions Students: A Scoping Review\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) has potential applications in almost every aspect of contemporary society (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Within healthcare, there are current and potential future uses for AI in many different specialties including radiology (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), cardiology (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), dentistry (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), nursing (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and pharmaceutical drug discovery (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). There is an expectation from the general public that the health sector will embrace this innovation in order to provide digitally-powered 21st century healthcare to patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, there are significant challenges that must be overcome when attempting to realise this potential and integrate AI into real-world healthcare practice.\u003c/p\u003e \u003cp\u003eOne such challenge is ensuring that the healthcare workforce has the appropriate knowledge and skills required to use AI tools safely and effectively in their practice. Integration of digital literacy into the education of health professions students (HPS) has been called for on a national level in several countries including the United States (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), United Kingdom (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and Canada (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, despite students expressing an eagerness to learn about AI, few universities provide their HPS with training in this subject (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome attempt has been made to identify what and how to teach AI in medical education (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, there is no clear consensus on which competencies and educational methods should be used to teach students to become AI proficient and there is much in this area that still remains uncharted. In addition, there is little understanding of how best to teach AI to non-medical health professions students such as those in dentistry, optometry, or pharmacy. Furthermore, the relative novelty of this subject area means that the rate of publication about AI in healthcare is increasing rapidly. Therefore, there is a clear need to consolidate the most recent evidence available in this subject area, as previous synthesis of the literature may quickly become out of date.\u003c/p\u003e \u003cp\u003eConsidering this landscape, a scoping review was undertaken to map key themes and identify gaps in the available literature on how best to teach and prepare HPS about AI for the digitally-powered healthcare practice of the near future.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eThis scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and the Arksey and O\u0026rsquo;Malley five-stage framework for scoping reviews (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eStage 1: Identifying the Research Question\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat topics related to AI have been taught to HPS in universities?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat educational methods have been employed in universities to teach HPS about AI?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eStage 2: Identifying Relevant Studies\u003c/h3\u003e\n\u003cp\u003eSearch strategies for PubMed/Medline, Scopus, CINAHL and ERIC were developed by the authors with the assistance of a health and human sciences academic librarian. Appropriate subject headings, keywords and free text terms relating to AI education for HPS were used in combination with Boolean operators to achieve optimal results. No restrictions on date or language were applied during the searches. For complete search strategies used, see Appendix 1.\u003c/p\u003e \u003cp\u003eThe population concept context (PCC) framework was employed as a guide for developing the search strategies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCC Framework.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudents in health professions, including (but not limited to) medicine, nursing, dentistry, pharmacy, biomedical science, allied health professions, optometry, radiography.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConcept\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTeaching health professions students about artificial intelligence, either generally or AI as a medical device (AIaMD).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContext\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity setting.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe searches were run and results downloaded on 10th July 2024. Additional relevant sources were identified using citation pearl growing. Results of the search strategy were uploaded to Covidence (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStage 3: Study Selection\u003c/h2\u003e \u003cp\u003eA two-step screening process was conducted which consisted of, firstly, title and abstract screening and, thereafter, full text review.\u003c/p\u003e \u003cp\u003eFor a study to be included, the following eligibility criteria was required to be met:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDiscussed an educational intervention which had actually been implemented.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHPS took part in the educational intervention.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe educational intervention was about AI, either generally or AIaMD.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe educational intervention took place in a university setting.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe exclusion criteria encompassed studies that:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTaught fully-qualified health professionals and not HPS.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTaught non-health professions students.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTook place outside of a university setting.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProposed a training course or educational intervention but did not actually implement it.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTaught digital health competencies which did not specifically include AI (e.g. robotics).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUsed AI to teach health professions students (e.g. virtual simulations, virtual tutors) without teaching any aspect of how the AI tool itself worked.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMore than 10 years old.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFull text was not available.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDid not have a full text in English.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eUsing Covidence, duplicate records were eliminated (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The first step of the screening process (title and abstract screening) was conducted by a single reviewer (FB). Following this, the second step of the screening process (full text review) was initially conducted by the first reviewer (FB). During the full text review, any studies where the eligibility was unclear or questionable were marked as \u0026ldquo;Maybe\u0026rdquo; and were reassessed by a second reviewer (LC). Both reviewers then discussed these studies to reach a consensus.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStage 4: Charting the Data\u003c/h3\u003e\n\u003cp\u003eIn order to answer the two research questions, an iterative process was used to create a charting form to extract data from the included studies. Data was extracted in the following domains: article details, study details, educational intervention details, implementation factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Inductive coding was used to code study details, educational intervention details and implementation factors. The charting form was initially piloted on five studies to ensure that all data relevant to the research questions were extracted.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDomains and subdomains for data extraction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubdomain\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArticle details\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy type, year and location.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy details\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation, institution, intervention evaluation method.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational intervention details\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName, duration, mode of delivery, pedagogical theories, educational methods, topics included, student evaluation methods.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplementation factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacilitators and barriers.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs the review progressed, if the reviewers wished to extract additional information of relevance, further revisions to the charting form could be made iteratively as required.\u003c/p\u003e\n\u003ch3\u003eStage 5: Collating, Summarising and Reporting the Results\u003c/h3\u003e\n\u003cp\u003eA narrative review approach was used to collating, summarising and reporting the results (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A numerical analysis using descriptive statistics was used to report on each domain. Tables and charts were produced in order to map the geographic distribution and chart the key characteristics of the studies, and a thematic analysis was conducted. Curriculum topics were coded and then grouped using a modified version of McCoy et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u0026rsquo;s domains regarding what physicians need to understand about AI.\u003c/p\u003e \u003cp\u003eAs this scoping review was following the recommendations by Arksey and O\u0026rsquo;Malley (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), it was not deemed necessary to perform a systematic quality appraisal of the studies included. However, whilst a quality appraisal was not undertaken, the reviewers ensured that all included studies declared either approval or an appropriate ethics exemption granted by an institutional review board or ethics committee.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy Characteristics\u003c/h2\u003e \u003cp\u003eOur search identified 10,979 unique titles, of which 15 studies were included in the final analysis (\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost of the included studies originated from either the United States (n\u0026thinsp;=\u0026thinsp;6, 40.0%) or Germany (n\u0026thinsp;=\u0026thinsp;4, 26.7%), although there was generally a diverse geographic spread which included Belgium (n\u0026thinsp;=\u0026thinsp;1, 6.7%), Finland (n\u0026thinsp;=\u0026thinsp;1, 6.7%), Puerto Rico (n\u0026thinsp;=\u0026thinsp;1, 6.7%), Turkey (n\u0026thinsp;=\u0026thinsp;1, 6.7%), and the United Kingdom (n\u0026thinsp;=\u0026thinsp;1, 6.7%). Despite the selection criteria allowing the inclusion of studies published within the last 10 years, all included studies had been published since the year 2020, with most of the included studies published in either 2023 (n\u0026thinsp;=\u0026thinsp;5, 33.3%) or 2024 (n\u0026thinsp;=\u0026thinsp;6, 40.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePublication and educational intervention titles of the included studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitle (Author, Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName of Educational Intervention\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study. (Abid et al., 2024) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmory University School of Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName not reported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAn Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum. (Culp et al., 2024) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Southern California\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName not reported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI for Doctors - A Course to Educate Medical Professionals in Artificial Intelligence for Medical Imaging. (Hedderich et al., 2021) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical University of Munich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI for Doctors: Medical Imaging\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssessing the Impact of AI Education on Hispanic Healthcare Professionals\u0026rsquo; Perceptions and Knowledge. (Heredia-Negr\u0026oacute;n et al., 2024) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Puerto Rico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArtificial Intelligence and Machine Learning Applied to Health Disparities Research (AIML\u0026thinsp;+\u0026thinsp;HDR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrounded in reality: artificial intelligence in medical education. (Krive et al., 2023) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Illinois at Chicago\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnalytics and Artificial Intelligence in Medicine (A2IM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis. (Kr\u0026ouml;plin et al., 2024) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Rostock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital Health - Digitalisation and Digital Transformation of Medicine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropagating AI Knowledge Across University Disciplines- The Design of A Multidisciplinary AI Study Module. (Laato et al., 2020) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Turku\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI in Diagnostics, Pharmaceutics and Imaging (Biomedicine); AI in Nursing Sciences (Nursing)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArtificial intelligence in medical education and the meaning of interaction with natural intelligence - an interdisciplinary approach. (Lang \u0026amp; Repp, 2020) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJustus-Liebig-University Gie\u0026szlig;en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Natural) Science and Technology in Medicine \u0026ndash; SciTecMed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect of a flipped classroom course to foster medical students\u0026rsquo; AI literacy with a focus on medical imaging: a single group pre-and post-test study. (Laupichler et al., 2022) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBonn Medical School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKI-LAURA (Artificial intelligence in the teaching of ophthalmology and radiology)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system. (Luo et al., 2024) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthwestern University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName not reported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA Pilot Remote Curriculum to Enhance Resident and Medical Student Understanding of Machine Learning in Healthcare. (Meade et al., 2023) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase Western Reserve University School of Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMachine Learning in Healthcare\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study. (Park et al., 2024) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeisel School of Medicine at Dartmouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital Health Scholars (DHS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrating Artificial Intelligence into Medical Education: Lessons Learned From a Belgian Initiative. (Pizzolla et al., 2023) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Mons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI and Digital Medicine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect of Artificial Intelligence Course in Nursing on Students' Medical Artificial Intelligence Readiness: A Comparative Quasi-Experimental Study. (Taskiran, 2023) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAydin Adnan Menderes University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName not reported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArtificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study. (Van De Venter et al., 2023) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity University London\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntroduction to Artificial Intelligence for Radiographers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFollowing analysis of the descriptive elements, a deeper contextualisation of the studies led to the derivation of a number of themes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWhich healthcare specialties are being taught about AI?\u003c/h2\u003e \u003cp\u003eMost of the included studies taught medical students about AI (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Other health professions students taught about AI were in nursing (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), biomedicine (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), medical biotechnology (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), pharmacy (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), dentistry (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), radiography (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) or were non-specified health-related students (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Only two of the included studies taught students from more than one health discipline (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Some of the included studies taught qualified health professionals or taught student populations from other non-health disciplines in addition to HPS (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). For the purposes of this scoping review, only the information about university-level HPS was extracted and synthesised from these studies.\u003c/p\u003e \u003cp\u003eThe number of students who completed the educational intervention varied across the included studies. The most common student population size was around 20 students (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). However, student population sizes as low as three (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and as high as 170 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) were reported (Appendix 2).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWhat are health professions students being taught about AI?\u003c/h3\u003e\n\u003cp\u003eThe included studies delivered educational interventions which covered a broad range of topics about AI. The initial intention of this study was to frame topics based on the three domains from McCoy et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) regarding what clinicians need to understand about AI in a clinical context: how to use it, interpret it, and explain it. Of these AI topics, 10 fell under the \u003cem\u003euse it\u003c/em\u003e domain, three fell under the \u003cem\u003einterpret it\u003c/em\u003e domain, and one fell under the \u003cem\u003eexplain it\u003c/em\u003e domain. However, the three-domain framework by McCoy et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) was not fully inclusive of all the thematic elements that arose from the literature. Four topics did not fit into the \u003cem\u003euse it, interpret it, explain it\u003c/em\u003e categorisation, and so an additional \u003cem\u003einnovate it\u003c/em\u003e domain was created (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most common topics covered were foundational AI literacy, which covered basic AI concepts and terminologies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29 CR30\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), and integrating and applying AI to healthcare practice (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29 CR30 CR31\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Ten studies had educational interventions which included how AI learns or is trained as a topic (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Programming (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), the ethical issues surrounding the use of AI (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and AI in medical imaging (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) were included in six studies each. The full range of topics covered by the educational interventions in the included studies can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTopics covered in the educational interventions of the included studies framed by a modified version of McCoy et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u0026rsquo;s domains.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%) of studies\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eUse it\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoundational AI literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrating and applying AI to healthcare practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics and AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 30, 32, 34, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI in medical imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 26, 29, 30, 31, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI in clinical decision support / diagnostics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26, 29, 31, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaw / regulation / governance and AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpact of AI on medicine and/or nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictive analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData protection and information security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenerative AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInterpret it\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHow AI learns / is trained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 26, 27, 29, 30, 33, 34, 35, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimitations / pitfalls of AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27, 29, 30, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAI in health disparities / inequalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28, 33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExplain it\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstanding and interpreting AI research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27, 28, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInnovate it\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgramming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27, 28, 30, 33, 34, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBioinformatics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetworking with AI industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther digital health competencies (e.g. virtual reality, robotics, telemedicine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25, 32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNot specified\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMost studies published a partial curriculum from their educational intervention (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Two studies published a full curriculum (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and three studies did not publish any curriculum (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eHow are health professions students being taught about AI?\u003c/h3\u003e\n\u003cp\u003eThe included studies had educational interventions which were delivered in a wide range of formats. Most of the studies delivered their educational intervention with some online format, either fully online (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) or in a hybrid format (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The majority of educational interventions were delivered as an elective course or module (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The most common duration was around three months (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), the approximate length of a single university semester. However, the duration varied from as short as two weeks (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) to as long as seven months (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA wide variety of educational methods were used to teach the AI courses. Reading, such as scientific articles, textbooks or websites, was the most common educational method employed (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35 CR36\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Lectures, either live (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) or pre-recorded (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), were another educational method which was frequently used. By contrast, educational methods such as seminars (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and simulated case scenarios (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) were used by relatively few studies, although this may be due to a lack of consistency in naming educational methods used between the studies, as it is unclear how seminars may differ from other methods used by other studies such as tutorials (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The full range of educational methods in the included studies can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eOnly one study created an educational intervention solely using pre-existing educational resources and did not develop any new learning materials for the course (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the educational interventions in the included studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%) of studies\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIn-Person or Online\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 28, 29, 33, 34, 36, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25, 32, 35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26, 27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24, 30, 31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElective or Mandatory\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMandatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24, 26, 35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;2 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;1 month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28, 36, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 29, 31, 32, 33, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24, 26, 30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Methods Used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (66.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 27, 28, 29, 33, 34, 35, 36, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLive lectures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 27, 28, 29, 32, 34, 35, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup discussions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 24, 26, 27, 29, 30, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnline videos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 28, 29, 33, 36, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-recorded lectures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 26, 28, 33, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractical tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26, 27, 29, 35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemonstrations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27, 29, 30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen-book multiple choice questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26, 34, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTutorials\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-existing Massive Open Online Course (MOOC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimulated case scenarios\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeminars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePodcasts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry-led workshops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNew Learning Materials Created\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePedagogical Theories Applied\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlipped classroom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 29, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructivism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBackwards design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrative learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24, 25, 26, 28, 30, 31, 32, 33, 35, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStudent Evaluation Methods Used\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual project or term paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23, 28, 29, 32, 36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26, 28, 35, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCQs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26, 29, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reflective exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone reported\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24, 25, 27, 30, 31, 33, 34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRelatively few studies reported on the pedagogical theories applied to underpin their educational intervention. Flipped classroom was the most commonly reported pedagogical theory and was used by three of the included studies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEight of the included studies reported on methods that were used to evaluate students\u0026rsquo; performances in the AI courses. Individual assignments were used by five studies which included term papers, literature reviews, project proposals, and programming exercises (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Group projects were employed by four studies which included creating videos about AI topics, oral presentations, and programming exercises (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Multiple choice questionnaires were used by three studies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). One study reported using weekly self-reflective exercises which were reviewed by faculty (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHow are AI courses for health professions students being evaluated?\u003c/h2\u003e \u003cp\u003eMost studies used non-validated subjective surveys to evaluate their educational intervention, either pre- and post-course (\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) or post-course only (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) (Appendix 2). Only two studies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) made use of Karaca et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e)\u0026rsquo;s pre-existing validated Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). Qualitative analysis of the educational intervention was conducted by one study using semi-structured interviews (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies discussed facilitators and barriers to implementing their educational interventions (Appendix 2). The most common facilitator reported was a high level of interest about AI among HPS (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The most commonly reported barrier was a lack of student time (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). This lack of time may have been due to a busy existing curricular schedule, concurrent examinations for other modules, or an underestimation of the time effort required to complete the course.\u003c/p\u003e \u003cp\u003eAll extracted data from the included studies have been categorised into themes, with no extracted data omitted from thematic grouping.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis scoping review collated and synthesised the available evidence regarding teaching health professions students in university settings about artificial intelligence. A deeper understanding of what is being taught about AI and how AI is being taught to HPS was gained. However, the available literature in this field is still limited and significant gaps remain in understanding the best way to teach HPS about AI.\u003c/p\u003e \u003cp\u003ePrevious reviews in this area have focused on the integration of AI into medical education (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Whilst medical students were the most common health discipline in this scoping review, present in 10 out of the 15 included studies, this review also included AI educational interventions taught to students in nursing, pharmacy, dentistry, radiography, medical biotechnology, biomedicine and non-specific health related students. The inclusion of a broader range of health specialties may make the results of this present study more generalisable across health disciplines. However, whilst this study did include a wider range of health professions than in previous reviews, many more health disciplines such as optometry, audiology, or occupational therapy are not represented in the existing literature to date.\u003c/p\u003e \u003cp\u003eUnlike previous reviews on AI in health education, this scoping review did not include any perspective or commentary articles or proposed curricula, but only those which had actually implemented a manifest educational intervention about AI to HPS (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Despite this distinction in the types of studies included, a similar lack of consensus on the optimal way to teach HPS about AI was found. This was illustrated by the broad range of topics about AI covered, educational methods employed and course durations in the included studies. Some of the topics which were frequently covered, such as foundational AI literacy (13 out of 15 studies) and applying AI to healthcare practice (13 out of 15 studies), align closely with the topics identified as highly important to include in medical AI education by Civaner et al.(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) in their cross-sectional needs assessment. However, other highly important topics identified by this needs assessment, such as training to prevent and solve ethical problems that may arise with AI applications, were included by relatively few studies (6 out of 15 studies) in this scoping review.\u003c/p\u003e \u003cp\u003eThe topics covered by the educational interventions included in this scoping review were categorised based on a modified version of McCoy et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u0026rsquo;s domains. McCoy et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) suggested that in order to understand how to deploy AI in a clinical context, physicians should learn how to use it, interpret it and explain it. Most of the topics covered by the educational interventions in this review were categorised under these three domains, however, four of the topics covered did not appear to fit under the \u003cem\u003euse it\u003c/em\u003e, \u003cem\u003einterpret it\u003c/em\u003e, \u003cem\u003eexplain it\u003c/em\u003e categorisation. These topics, which included programming and networking with AI industry, could be considered as not necessary for healthcare practitioners to know in order to apply AI tools to their everyday clinical practice, but may be useful to learn for clinicians who wish to drive innovation in AI. Therefore, an additional fourth domain of \u003cem\u003einnovate it\u003c/em\u003e, was created for the categorisation of the AI topics in this review. These \u003cem\u003einnovate it\u003c/em\u003e topics may be more well-suited to an extracurricular program targeted towards those HPS with a particular interest in AI who wish to be AI innovators. This approach has already been demonstrated by one of the included studies in this scoping review, where technical and non-technical tracks were created for the AI course and programming was only taught to students on the technical track (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This approach does have some limitations, however, as Abid et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) found that very few HPS possessed the requisite pre-existing knowledge needed to undertake the technical track of their AI course.\u003c/p\u003e \u003cp\u003eMost of the included studies (12 out of 15 studies) made some effort to evaluate the effectiveness of their interventions and reported generally positive results in achieving their respective aims. The most common evaluation method was the use of pre- and post-intervention surveys (9 out of 15 studies). This is similar to the findings of Charow et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) and Doherty et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Despite the widespread use of pre- and post-surveys, these were generally non-validated and measured a wide range of mostly subjective parameters. The lack of consistency between the evaluation methods of the included studies makes comparisons between these studies difficult. In order to achieve more objective comparison of educational intervention effectiveness between studies, standardised and validated instruments such as the MAIRS-MS should be utilised (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The MAIRS-MS was utilised by 2 out of 15 included studies of this scoping review, although it was applied in different ways. In one study, the MAIRS-MS was used to compare students who had undergone an AI educational course to a control group who received no AI education (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), whereas in the other study, students took the MAIRS-MS twice and their results pre- and post-course were compared (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Therefore, although a standardised instrument was used, due to the differences in application, a direct comparison between these two studies is still difficult. The MAIRS-MS instrument has some limitations, such as its original intended purpose to measure status quo in AI readiness at a single point in time rather than to compare between different points in time before and after an educational intervention. However, the more widespread use of a standardised instrument such as MAIRS-MS applied in a standardised manner would allow for more constructive comparisons to be made between studies and therefore more robust conclusions to be drawn on which educational interventions are most effective.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe findings of this scoping review should be considered within the context of its limitations. Due to the large volume of studies generated by the search results, the first step of the screening process was completed only by a single reviewer and was not duplicated by a second reviewer. To account for this, the first reviewer used a low threshold for sending studies to full-text review. During the full-text review, any studies whose eligibility for inclusion was considered questionable or borderline were reviewed by a second reviewer, discussed, and a consensus between reviewers was reached.\u003c/p\u003e \u003cp\u003eIn addition, the search for this scoping review was performed on 10th July 2024, however the rate of publication in this area is increasing rapidly. There are likely to be further relevant studies which have been published in this area since the search date which have not been included in this review. Therefore, a repeat of this scoping review may be performed in future to provide an updated synthesis of the literature in this area.\u003c/p\u003e \u003cp\u003eFurthermore, whilst the research team comprised of members with expertise in AI and health professions education, the sixth stage of the Arksey and O\u0026rsquo;Malley scoping review framework of external expert consultation was not performed, which could be viewed as a limitation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eArtificial intelligence is expected to revolutionise the healthcare landscape in the coming years and health professions students must be adequately prepared for this. It is imperative that universities providing health professions degrees keep pace with AI innovations and provide appropriate training in AI literacy for their students. This review of the available literature has found that whilst some university programs are already implementing AI educational interventions for their health professions students, there remains a severe lack of consensus on what and how to teach about AI to HPS. Future educational interventions should use an evidence-based approach in the design, implementation and evaluation of their curricula for the inclusion of AI into HPS training. Further research should be conducted to build this evidence base, particularly in teaching students from a wider range of health disciplines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRawas S, AI (2024) -03-26;4(1):25 The future of humanity. Discov Artif Intell [Internet]. 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JMIR Publications Inc.. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/31043\u003c/span\u003e\u003cspan address=\"10.2196/31043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCivaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A (2022) Artificial intelligence in medical education: A cross-sectional needs assessment. BMC Medical Education [Internet]. [cited Jul 28, 2024];22:772. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bmcmededuc.biomedcentral.com/articles/\u003c/span\u003e\u003cspan address=\"https://bmcmededuc.biomedcentral.com/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12909-022-03852-3\u003c/span\u003e\u003cspan address=\"10.1186/s12909-022-03852-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoherty G, McLaughlin L, Hughes C, McConnell J, Bond R, McFadden S A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff. Radiography [Internet]. 2024-03-01;30(2):474\u0026ndash;82. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/pii/S1078817423002596\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/pii/S1078817423002596\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Huddersfield","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, machine learning, medical education, healthcare education","lastPublishedDoi":"10.21203/rs.3.rs-6972197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6972197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rapid pace at which artificial intelligence (AI) technologies are being integrated into healthcare demands competency on the part of health professionals in how to effectively integrate these tools into their practice. However, not many universities currently teach health professions students (HPS) about AI. A scoping review was undertaken to map key themes and identify gaps in the available literature on how best to teach HPS about AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis scoping review followed the PRISMA-ScR checklist and the Arksey and O’Malley five-stage framework. The aim was to discover what AI topics have been taught to HPS and what educational methods have been employed to teach HPS about AI. A search of 4 databases (PubMed, Scopus, CINAHL, ERIC) identified 10,979 unique titles which underwent a two-step screening process and 15 full text studies were included. Data were extracted in an iterative process. A narrative review approach was used to generating themes and reporting results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMost of the included studies taught medical students about AI, although students from other health specialties such as nursing, pharmacy and dentistry also appeared in the literature. A broad range of topics about AI were delivered by the educational interventions which were synthesised using a modified framework from McCoy et al. (2020). The most frequent topics taught were foundational AI literacy and applying AI to healthcare practice. A wide variety of teaching methods were utilised, most commonly reading and lectures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhilst some university programs are already implementing AI educational interventions for their health professions students, there remains a lack of consensus on what and how to teach about AI to HPS. Further research should be conducted to build an evidence base for the design, implementation and evaluation of AI curricula for HPS, particularly in teaching students from a wider range of health disciplines.\u003c/p\u003e","manuscriptTitle":"Artificial Intelligence Education for Health Professions Students: A Scoping Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 05:39:18","doi":"10.21203/rs.3.rs-6972197/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62f7190d-0693-4367-8251-11a4a5cc3c76","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50563653,"name":"Artificial Intelligence and Machine Learning"},{"id":50563654,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2025-06-26T05:39:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-26 05:39:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6972197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6972197","identity":"rs-6972197","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}