Cited by 101Artificial Intelligence in Healthcare and EducationArtificial Intelligence in EducationInnovations in Medical Education

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Harnessing the potential of large language models in medical education: promise and pitfalls

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Benítez, Trista M, Xu, Yueyuan, Boudreau, J Donald, Kow, Alfred Wei Chieh, Bello, Fernando, Van Phuoc, Le, Wang, Xiaofei, Sun, Xiaodong, Leung, Gilberto Ka-Kit, Lan, Yanyan, Wang, Yaxing, Cheng, Davy, Tham, Yih-Chung, Wong, Tien Yin, Chung, Kevin C·2024·Journal of the American Medical Informatics Association

DOI 10.1093/jamia/ocad252PMC PMC10873781

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Abstract

Objectives: To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum. Process: Narrative review of published literature contextualized by current reports of LLM application in medical education. Conclusions: LLMs like OpenAI’s ChatGPT can potentially revolutionize traditional teaching methodologies. LLMs offer several potential advantages to students, including direct access to vast information, facilitation of personalized learning experiences, and enhancement of clinical skills development. For faculty and instructors, LLMs can facilitate innovative approaches to teaching complex medical concepts and fostering student engagement. Notable challenges of LLMs integration include the risk of fostering academic misconduct, inadvertent overreliance on AI, potential dilution of critical thinking skills, concerns regarding the accuracy and reliability of LLM-generated content, and the possible implications on teaching staff.

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Authors: Benítez, T. M., Xu, Y., Boudreau, J. D., Kow, A. W. C., Bello, F., Van Phuoc, L., Wang, X., Sun, X., Leung, G. K.-K., Lan, Y., Wang, Y., Cheng, D., Tham, Y.-C., Wong, T. Y., & Chung, K. C.
Affiliations: University of Michigan (US)
Tsinghua University (CN)
McGill University (CA)
National University of Singapore (SG)
College of Health Sciences, VinUniversity , Hanoi, 100000, Vietnam
Beihang University (CN)
Shanghai Jiao Tong University (CN)
Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital , Hong Kong, 999077, China
Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing Ophthalmology and Visual Sciences Key Laboratory, Capital University of Medical Science , Beijing, 100730, China
Chinese University of Hong Kong, Shenzhen (CN)
Singapore National Eye Center (SG)
Corresponding author: Tham, Yih-Chung
Chung, Kevin C
ORCID: Boudreau, J Donald
Kow, Alfred Wei Chieh
Bello, Fernando
Wang, Xiaofei
Sun, Xiaodong
Lan, Yanyan
Cheng, Davy
Tham, Yih-Chung
Wong, Tien Yin
Chung, Kevin C
Venue: Journal of the American Medical Informatics Association
Publisher: Oxford University Press (OUP)
Published: 2024-01-24
Volume: 31
Issue: 3
Pages: 776-783
ISSN: 1067-5027 (ISSN-L)
1527-974X
DOI: 10.1093/jamia/ocad252
PMC: PMC10873781
OpenAlex: W4391225179
Cited by: 100
References: 51
Citations by year: 2026: 25 · 2025: 55 · 2024: 19
FWCI: 2.26
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: other-oa
academic.oup.com/pages/standard-publication-reuse-rights (vor)
Locations: Journal of the American Medical Informatics Association (publishedVersion)
PubMed Central (publishedVersion, other-oa)
PubMed (publishedVersion)
The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
Funders: National Key Research and Development Program of China (2022YFC2502802)
National Natural Science Foundation of China (8238810007)
National Institutes of Health
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Social Sciences › Education › Artificial Intelligence in Education
Social Sciences › Education › Innovations in Medical Education
Keywords: large language models, ChatGPT, medical education, AI in teaching, personalized learning, clinical skills development, academic misconduct, overreliance on AI, critical thinking, medical curriculum
MeSH terms: Clinical Competence, Education, Medical, Humans, Language, Learning, Reproducibility of Results

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