自动化大型语言模型的专家级医学推理评估
Shuang Zhou1, Wenya Xie2, Jiaxi Li3
1Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
NPJ digital medicine
|December 6, 2025
概括
一个新的基准,MedThink-Bench,严格评估大型语言模型 (LLM) 的医学推理. 该LLM-w-Rationale框架提供了可扩展的,专家级别的LLM推理质量的评估.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地用于临床决策.
- 目前的医学推理LLM评估方法缺乏严谨性和可扩展性.
- 需要一个标准化的基准来评估在LLMs医疗推理.
研究的目的:
- 引入MedThink-Bench,这是对LLM医疗推理能力的严格和可扩展评估的基准.
- 介绍LLM-w-Rationale,这是一个评估框架,用于评估专家级忠实性和可扩展性的LLM推理质量.
主要方法:
- 开发了MedThink-Bench,包含10个领域的500个高度复杂的医学问题.
- 包括专家撰写的每个问题的逐步推理.
- 引入了LLM-w-Rationale,将精细的理由评估与LLM-as-a-Judge方法相结合.
主要成果:
- LLM-w-Rationale与专家评估有很强的相关性 (皮尔森系数高达0.87).
- 在评估推理质量方面,LLM-w-Rationale框架实现了专家级别的准确性.
- 评估时间缩短到传统专家评估时间的1.4%.
结论:
- MedThink-Bench为评估LLM医学推理提供了一个严格且可扩展的标准.
- 通过LLM-w-Rationale,可以有效和可靠地评估LLM推理质量.
- 这些进展支持在临床实践中安全和负责任地部署LLM.
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