Generative AI in Undergraduate Medical Education: A Rapid Review

Abstract

OBJECTIVES Generative artificial intelligence (AI) models such as OpenAI's ChatGPT and Google's Bard have forced educators to consider how these tools will be efficiently utilized to improve medical education. This article investigates current literature on how generative AI is and could be used and implemented in undergraduate medical education (UME). METHODS A rapid review of the literature was performed utilizing a librarian-generated search strategy to identify articles published before June 30, 2023, in 6 databases (Pubmed, EMBASE.com, Scopus, ERIC via EBSCO, Computer Science Database via EBSCO, and CINAHL via EBSCO). Inclusion criteria were (1) a focus on osteopathic and/or allopathic UME and (2) a defined use or implementation strategy for generative AI. Two reviewers screened all articles, and data extraction was performed by 1 reviewer and confirmed by the other reviewer. RESULTS A total of 521 relevant articles were screened during this review. Forty-one articles underwent full-text review and data extraction. The majority of the articles were opinion pieces (9), case reports (8), letters to the editor (5), editorials (5), and commentaries (3) about the use of generative AI while 7 articles used qualitative and/or quantitative methods. The literature is best divided into 5 categories of uses for generative AI in UME: nonclinical learning assistant, content developer, virtual patient interaction, clinical decision-making tutor, and medical writing. The literature indicates generative AI tools’ greatest potential is for use as a virtual patient and clinical decision-making tutor. CONCLUSIONS While the possibilities proliferate for generative AI in UME, there remains a dearth of quantitative evidence of its use for improving learner outcomes. The majority of the literature opines the potential for utilization, but only 7 studies formally evaluated the results of using generative AI. Future research should focus on the effectiveness of incorporating generative AI into preclinical and clinical curricula in UME.