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AI in Medical Education: Global situation, effects and challenges

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Abstract

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.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This paper is supported by National Natural Science Foundation of China (Project No. 72104087, 72004070) and University-Industry Collaborative Education Program supported by Ministry of Education in China (220505084312449).

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Correspondence to Chengyan Zhu.

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Zhang, W., Cai, M., Lee, H.J. et al. AI in Medical Education: Global situation, effects and challenges. Educ Inf Technol 29, 4611–4633 (2024). https://doi.org/10.1007/s10639-023-12009-8

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  • DOI: https://doi.org/10.1007/s10639-023-12009-8

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