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Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives
Open accessAbstract
Objective: Medical students, as clinicians and healthcare leaders of the future, are key stakeholders in the clinical roll-out of artificial intelligence-driven technologies. The authors aim to provide the first report on the state of artificial intelligence in medical education globally by exploring the perspectives of medical students. Methods: The authors carried out a mixed-methods study of focus groups and surveys with 128 medical students from 48 countries. The study explored knowledge around artificial intelligence as well as what students wished to learn about artificial intelligence and how they wished to learn this. A combined qualitative and quantitative analysis was used. Results: Support for incorporating teaching on artificial intelligence into core curricula was ubiquitous across the globe, but few students had received teaching on artificial intelligence. Students showed knowledge on the applications of artificial intelligence in clinical medicine as well as on artificial intelligence ethics. They were interested in learning about clinical applications, algorithm development, coding and algorithm appraisal. Hackathon-style projects and multidisciplinary education involving computer science students were suggested for incorporation into the curriculum. Conclusions: Medical students from all countries should be provided teaching on artificial intelligence as part of their curriculum to develop skills and knowledge around artificial intelligence to ensure a patient-centred digital future in medicine. This teaching should focus on the applications of artificial intelligence in clinical medicine. Students should also be given the opportunity to be involved in algorithm development. Students in low- and middle-income countries require the foundational technology as well as robust teaching on artificial intelligence to ensure that they can drive innovation in their healthcare settings.
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“A need for integration of AI knowledge into core curricula has also been widely expressed among medical students78.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [78]
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“In contrast, a few of them employed either qualitative methods [38–40] or a mixed-methods approach [41–45].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [41]
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“The disparity in AI exposure between students in HICs and LMICs—where formal AI education remains limited or absent [24, 41]—underscores persistent global inequities in access to AI-related educational resources”
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Metadata
- Authors
- Ejaz, H., McGrath, H., Wong, B. L., Guise, A., Vercauteren, T., & Shapey, J.
- Affiliations
- University of East Anglia (GB)
- King's College London (GB)
- Department of International Health, Care and Public Health Research Institute, Maastricht University, Maastricht, Netherlands
- School of Population Health and Environmental Sciences, King’s College London, UK
- Corresponding author
- Ejaz, Hamza
- McGrath, Hari
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2022-01
- Volume
- 8
- Pages
- 205520762210890
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC9067043
- OpenAlex
- W4229033576
- Cited by
- 105
- References
- 43
- Citations by year
- 2026: 21 · 2025: 37 · 2024: 32 · 2023: 14
- FWCI
- 6.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Digital Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by-nc-nd)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion, cc-by)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Wellcome/EPSRC Centre for Medical Engineering ([WT203148/Z/16/Z])
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Innovations in Medical Education
- Social Sciences › Education › Artificial Intelligence in Education
- Keywords
- artificial intelligence in medical education, medical students' perceptions, AI curriculum, AI ethics education, algorithm development, multidisciplinary education, hackathons, low- and middle-income countries
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