大语言模型在医学教育中的实际应用与缺陷探究:叙述性回顾。

IF 1.7 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Advances in Medical Education and Practice Pub Date : 2025-04-18 eCollection Date: 2025-01-01 DOI:10.2147/AMEP.S497020
Rui Li, Tong Wu
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引用次数: 0

摘要

近年来,大型语言模型(llm)已成为医学教育的重要工具,引起了人们的广泛关注。它们在制定教学计划、生成互动材料和促进有效反馈机制方面为教育工作者提供必要的支持。此外,法学硕士在教育活动中提高学生的语言习得、写作能力和创造力。本文旨在探讨法学硕士在提高教师和学生的教育和学业成绩方面的实际应用,并提供具体的例子来证明其有效性。此外,我们解决了与LLM实施相关的固有挑战,并提出了可行的解决方案,以优化其使用。我们的研究为法学硕士在医学教育和研究中的广泛整合奠定了基础,确保了医学学习的最高标准,并最终确保了患者的安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Delving into the Practical Applications and Pitfalls of Large Language Models in Medical Education: Narrative Review.

Delving into the Practical Applications and Pitfalls of Large Language Models in Medical Education: Narrative Review.

Delving into the Practical Applications and Pitfalls of Large Language Models in Medical Education: Narrative Review.

Delving into the Practical Applications and Pitfalls of Large Language Models in Medical Education: Narrative Review.

Large language models (LLMs) have emerged as valuable tools in medical education, attracting substantial attention in recent years. They offer educators essential support in developing instructional plans, generating interactive materials, and facilitating efficient feedback mechanisms. Furthermore, LLMs enhance students' language acquisition, writing proficiency, and creativity in educational activities. This review aims to examine the practical applications of LLMs in enhancing the educational and academic performance of both teachers and students, providing specific examples to demonstrate their effectiveness. Additionally, we address the inherent challenges associated with LLM implementation and propose viable solutions to optimize their use. Our study lays the groundwork for the broader integration of LLMs in medical education and research, ensuring the highest standards of medical learning and, ultimately, patient safety.

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来源期刊
Advances in Medical Education and Practice
Advances in Medical Education and Practice EDUCATION, SCIENTIFIC DISCIPLINES-
CiteScore
3.10
自引率
10.00%
发文量
189
审稿时长
16 weeks
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