人工智能从临床实践日志中提取学习经验的准确性:观察性研究
Takeshi Kondo1,2, Hiroshi Nishigori1
1Center for Medical Education, Nagoya University Graduate School of Medicine, 65, Tsurumai-cho, Showa-ku, Nagoya city, Aichi, 466-8560, Japan, +81 052 7412111.
JMIR medical education
|October 15, 2025
概括
大型语言模型 (LLM) 可以高准确度地从学习日志中预测医学学生的临床经验,尽管可能会错过一些细节. 这种人工智能应用程序有望减少教育工作者的负担,并提高医学教育评估.
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
- 临床学习分析
背景情况:
- 改善临床教育需要了解学生的经验,但手动分析是繁的.
- 学习记录的自动分析和体验的可视化可以实现实时进度跟踪.
- 大型语言模型 (LLM) 显示了分析临床学习数据的潜力,但其准确性需要评估.
研究的目的:
- 从学习日志数据中评估LLMs在预测医学学生实际临床经验方面的准确性.
- 探索LLMs对临床职务实时进展跟踪的实用性.
主要方法:
- 分析了名古屋大学医学院医学学生的学习日志数据.
- OpenAI的ChatGPT,特别是GPT-4-turbo,被用来提取基于医学教育模型核心课程的经验.
- 开发了一个网络应用程序来自动化提取过程,并根据学生提供的纠正列表评估准确性.
主要成果:
- 这项研究涉及20名六年级医学学生,产生了40个数据集.
- 在预测临床经验方面,GPT-4-turbo表现出高的整体特异性 (99.34%),但适度的敏感性 (62.39%).
- 性能因类别而异,与手术 (56.36%) 相比,对症状 (45.43%) 和检查 (46.76%) 的敏感性较低.
结论:
- 像GPT-4-turbo这样的LLM可以从具有高特异性和中度灵敏性的学习日志中预测临床经验.
- 未来的改进可能包括改进的人工智能模型,学习日志的反机制以及与电子医疗记录的整合.
- 人工智能驱动的学习日志分析有可能减少评估负担并提高医学教育的质量.
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