在医学教育中使用尿道系统组织学评估进行大型语言模型的比较评估
Anikó Szabó1, Ghasem Dolatkhah Laein2
1Department of Anatomy and Genetics, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Scientific reports
|August 29, 2025
概括
大型语言模型 (LLM) 对医学教育有希望,但性能不同. 系统评估对于选择最好的人工智能工具来完成特定的教育任务至关重要,例如组织学问题和临床情景.
科学领域:
- 医疗教育技术
- 医疗保健中的人工智能
- 组织学教育
背景情况:
- 大型语言模型 (LLM) 对医学教育有潜在的好处.
- 需要对LLM的特定领域能力进行系统评估.
- 尿道组织学是医学培训的一个关键领域.
研究的目的:
- 在尿道系统组织学教育中对十三种大型语言模型进行比较评估.
- 评估两个不同的任务的LLM表现:多选题 (MCQ) 和临床场景生成.
- 使用多维框架分析LLM的能力.
主要方法:
- 在65个验证的MCQ和临床场景生成中评估了13个LLM.
- 通过准确性和解释质量 (相关性,全面性) 来评估MCQ的表现.
- 通过质量,复杂性,相关性,正确性和多样性来评估场景.
主要成果:
- 在各个LLM和任务中表现有显著差异.
- ChatGPT- o1获得了最高的MCQ准确度 (96. 31%).
- 克劳德3. 5在临床情景生成方面表现出色 (91. 4% 的最大得分).
- 所有的模型都显著超过了MCQ的随机猜测.
- 在场景生成中,模型显示的正确性高于质量.
- 术语频率分析显示出内容不平衡和遗漏.
结论:
- 虽然LLM对医学教育有很大的前景,但对特定的任务需要谨慎选择.
- 可靠的实施需要将模型与教育需求相匹配,并采用验证机制.
- 目前的LLM在产生教学上平衡的内容方面存在局限性.
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