大型语言模型用于在医学教育中生成关键特征问题
Yavuz Selim Kıyak1, Stanisław Górski2, Tomasz Tokarek2,3
1Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Türkiye.
Medical education online
|October 29, 2025
概括
开放AI是一个开放的AI.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 心脏病学培训心脏病学培训
背景情况:
- 目前开发关键特征问题 (KFQ) 的方法可能会耗时.
- 加拿大医学委员会为KFQ开发提供了指导方针.
- 大型语言模型 (LLM) 显示了自动化内容生成的潜力.
研究的目的:
- 评估由OpenAI的o3模型生成的KFQ的质量.
- 为人工智能生成的KFQ开发一个标准化的提示和评估指标.
- 评估人工智能产生的KFQ与既定开发准则的协调程度.
主要方法:
- 开发了一种与加拿大医学委员会的KFQ指南一致的通用提示.
- 使用OpenAI的o3模型和ESC指南生成了20个专注于心脏病的KFQ.
- 两个心脏病专家使用质量检查清单评估了KFQ;第三个解决了分歧.
主要成果:
- 100%的产生的KFQ被评为"可接受" (15%"接受原样",85%"接受小修改").
- 检查清单的整体遵守率达到93.7%,在关键特征定义和情景可信度方面得分高.
- 需要改进的领域包括"杀手"反应的纳入 (50%) 和分散注意力的合理性 (77.8%).
结论:
- 由结构化提示指导的LLM生成的KFQ满足了高质量标准,并进行了微小的修改.
- 人工智能辅助的工作流可以简化基于能力的医学教育 (CBME) 的KFQ开发.
- 专家审查对于人工智能辅助医学教育中的临床准确性和患者安全至关重要.
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