A Scoping Review of Artificial Intelligence in Medical Education: BEME Guide No. 84

Gordon, Morris orcid iconORCID: 0000-0002-1216-5158, Daniel, Michelle, Ajiboye, Aderonke, Uraiby, Hussein, Xu, Nicole Y., Bartlett, Rangana, Hanson, Janice, Haas, Mary, Spadafore, Maxwell et al (2024) A Scoping Review of Artificial Intelligence in Medical Education: BEME Guide No. 84. Medical Teacher . ISSN 0142-159X

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Official URL: https://doi.org/10.1080/0142159X.2024.2314198


Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research.

This rapid scoping review, conducted over 16 weeks, employed Arksey and O’Malley’s framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape.

The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education.

The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.

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