大型语言模型的有效性及其在产科和妇科教育中的潜力
Kyung Jin Eoh1, Gu Yeun Kwon2, Eun Jin Lee2
1Department of Obstetrics and Gynecology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea.
Obstetrics & gynecology science
|October 2, 2024
概括
像GPT-4这样的大型语言模型 (LLM) 在产科和妇科考试中表现强,匹配居民分数. 这些人工智能工具可以帮助医学教育,但应该补充,而不是取代人类的专业知识.
科学领域:
- 医疗教育中的人工智能
- 大型语言模型 (LLM) 应用程序
- 发电预训练变压器 (GPT) 技术技术
背景情况:
- 大型语言模型 (LLM) 在医学教育,特别是产科和妇科的实用性正在积极讨论中.
- 人工智能 (AI) 的最新进展需要在专业教育环境中对LLM能力进行评估.
研究的目的:
- 评估生成预训练变压器 (GPT) -3.5和GPT-4在理解产科和妇科相关的临床信息方面的性能.
- 评估LLM绩效对产科和妇科居民教育的潜在影响.
主要方法:
- 通过使用4年 (2020-2023) 的116个问题进行了比较分析,用于每年产科和妇科推广检查的4年 (2020-2023).
- 将GPT-3.5和GPT-4的表现与三家医院的100名住院人员的分数进行了比较.
- 需要图像解释的问题被排除在分析之外.
主要成果:
- GPT-4的平均得分为79.31 (SD,3.67),相当于居民平均得分 (79.12-83.60).
- GPT-3.5的平均得分为38.79 (SD,5.65),明显低于GPT-4和居民.
- 在产科特定问题中,GPT-4的平均得分为90.22 (SD,7.68),超过了GPT-3.5 (33.44,SD,10.18).
结论:
- GPT-4在临床信息理解,数据解释和产科和妇科问题解决方面表现出显著的能力.
- 医学专业课程显示出作为增强医学教育的有价值工具的潜力,提供补充的学习资源.
- 医学教育中LLM的整合应该优先增加人类的专业知识和临床判断,承认目前的局限性.
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