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医学教育中的ChatGPT及其他大语言模型——文献综述

ChatGPT and Other Large Language Models in Medical Education - Scoping Literature Review.

作者信息

Aster Alexandra, Laupichler Matthias Carl, Rockwell-Kollmann Tamina, Masala Gilda, Bala Ebru, Raupach Tobias

机构信息

Institute of Medical Education, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.

出版信息

Med Sci Educ. 2024 Nov 13;35(1):555-567. doi: 10.1007/s40670-024-02206-6. eCollection 2025 Feb.

Abstract

This review aims to provide a summary of all scientific publications on the use of large language models (LLMs) in medical education over the first year of their availability. A scoping literature review was conducted in accordance with the PRISMA recommendations for scoping reviews. Five scientific literature databases were searched using predefined search terms. The search yielded 1509 initial results, of which 145 studies were ultimately included. Most studies assessed LLMs' capabilities in passing medical exams. Some studies discussed advantages, disadvantages, and potential use cases of LLMs. Very few studies conducted empirical research. Many published studies lack methodological rigor. We therefore propose a research agenda to improve the quality of studies on LLM.

摘要

本综述旨在总结在大语言模型(LLMs)可用的第一年里,关于其在医学教育中应用的所有科学出版物。按照PRISMA循证综述的建议进行了一项范围综述。使用预定义的搜索词在五个科学文献数据库中进行了检索。搜索得到1509条初步结果,最终纳入了145项研究。大多数研究评估了大语言模型通过医学考试的能力。一些研究讨论了大语言模型的优点、缺点和潜在应用案例。进行实证研究的非常少。许多已发表的研究缺乏方法严谨性。因此,我们提出了一个研究议程,以提高关于大语言模型研究的质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2ff4/11933646/8875517fd474/40670_2024_2206_Fig1_HTML.jpg

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