AI in Medical Education: Global situation, effects and challenges

Purpose: Artificial Intelligence (AI) is transforming healthcare and shows considerable promise for the delivery of medical education. This systematic review provides a comprehensive analysis of the global situation, effects, and challenges associated with applying AI at the different stages of medical education. Methods: This review followed the PRISMA guidelines, and retrieved studies published on Web of Science, PubMed, Scopus, and IEEE Xplore, from 1990 to 2022. After duplicates were removed (n = 1407) from the 6371 identified records, the full text of 179 records were screened. In total, 42 records were eligible. Results: It revealed three teaching stages where AI can be applied in medical education (n = 39), including teaching implementation (n = 24), teaching evaluation (n = 10), and teaching feedback (n = 5). Many studies explored the effectiveness of AI adoption with questionnaire survey and control experiment. The challenges are performance improvement, effectiveness verification, AI training data sample and AI algorithms. Conclusions: AI provides real-time feedback and accurate evaluation, and can be used to monitor teaching quality. A possible reason why AI has not yet been applied widely to practical teaching may be the disciplinary gap between developers and end-user, it is necessary to strengthen the theoretical guidance of medical education that synchronizes with the rapid development of AI. Medical educators are expected to maintain a balance between AI and teacher-led teaching, and medical students need to think independently and critically. It is also highly demanded for research teams with a wide range of disciplines to ensure the applicability of AI in medical education.