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

TitleHarnessing the potential of large language models in medical education: promise and pitfalls
Authors
KeywordsChatGPT
large language models
medical education
Issue Date1-Mar-2024
PublisherOxford University Press
Citation
A Scholarly Journal of Informatics in Health and Biomedicine, 2024, v. 31, n. 3, p. 776-783 How to Cite?
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.


Persistent Identifierhttp://hdl.handle.net/10722/348260
ISSN
2023 Impact Factor: 4.7
2023 SCImago Journal Rankings: 2.123

 

DC FieldValueLanguage
dc.contributor.authorBenítez, Trista M.-
dc.contributor.authorXu, Yueyuan-
dc.contributor.authorBoudreau, J. Donald-
dc.contributor.authorKow, Alfred Wei Chieh-
dc.contributor.authorBello, Fernando-
dc.contributor.authorPhuoc, Le Van-
dc.contributor.authorWang, Xiaofei-
dc.contributor.authorSun, Xiaodong-
dc.contributor.authorLeung, Gilberto Ka Kit-
dc.contributor.authorLan, Yanyan-
dc.contributor.authorWang, Yaxing-
dc.contributor.authorCheng, Davy-
dc.contributor.authorTham, Yih Chung-
dc.contributor.authorWong, Tien Yin-
dc.contributor.authorChung, Kevin C-
dc.date.accessioned2024-10-08T00:31:17Z-
dc.date.available2024-10-08T00:31:17Z-
dc.date.issued2024-03-01-
dc.identifier.citationA Scholarly Journal of Informatics in Health and Biomedicine, 2024, v. 31, n. 3, p. 776-783-
dc.identifier.issn1067-5027-
dc.identifier.urihttp://hdl.handle.net/10722/348260-
dc.description.abstract<p>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.</p>-
dc.languageeng-
dc.publisherOxford University Press-
dc.relation.ispartofA Scholarly Journal of Informatics in Health and Biomedicine-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectChatGPT-
dc.subjectlarge language models-
dc.subjectmedical education-
dc.titleHarnessing the potential of large language models in medical education: promise and pitfalls-
dc.typeArticle-
dc.identifier.doi10.1093/jamia/ocad252-
dc.identifier.pmid38269644-
dc.identifier.scopuseid_2-s2.0-85185345007-
dc.identifier.volume31-
dc.identifier.issue3-
dc.identifier.spage776-
dc.identifier.epage783-
dc.identifier.eissn1527-974X-
dc.identifier.issnl1067-5027-

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