聊天GPT与人类作者:与虚拟患者进行临床推理培训的概念图的比较研究
Renata Szydlak1, Yavuz Selim Kiyak2, Inga Hege3
1Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Kraków, Poland.
Medical teacher
|November 12, 2025
概括
聊天GPT可以为临床推理 (CR) 培训创建临床准确的概念图. 虽然需要专家的精细化来提高教育的清晰度,但大型语言模型 (LLM) 显示出开发CR学习资源的前景.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 认知科学 认知科学
背景情况:
- 临床推理 (CR) 是医疗保健专业人员的核心能力.
- 为CR培训开发有效的教育资源至关重要,但资源密集.
- 像ChatGPT这样的大型语言模型 (LLM) 提供了自动化内容创建的潜力.
研究的目的:
- 评估ChatGPT能够为CR培训生成临床准确和教育上有价值的概念图的能力.
- 评估LLMs作为创建高质量的CR教育材料的工具的潜力.
主要方法:
- 使用了来自欧洲iCoViP项目的10个虚拟病例.
- 聊天GPT生成的概念图与使用结构指标,专家对临床内容质量评估和教育工作者对教学实用性评估的专家创建的地图进行了比较.
- 统计分析包括Student的t测试和Cohen的卡帕,用于测量器间的可靠性.
主要成果:
- 聊天GPT地图的结构复杂性明显高 (概念和连接更多) 比专家地图 (p < 0.001).
- 在ChatGPT生成和专家地图之间,临床内容质量是可比的,有效性评级没有显著差异.
- 教育审查表明,ChatGPT地图缺乏抽象和优先级,可能超过学习者的认知负载.
结论:
- 聊天GPT可以为CR培训生成临床准确的概念图.
- 专家精细化是必要的,以解决教育清晰度和可用性的局限性.
- 随着监督,LLM可以帮助有效开发CR学习资源.
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