基于LLM的医疗文件评估:整合人类专家的洞察力
Junhyuk Seo1,2, Dasol Choi3,4, Wonchul Cha2,5
1Department of Nursing, Samsung Medical Center.
Studies in health technology and informatics
|August 8, 2025
概括
我们为医疗文件创建了一个大型语言模型 (LLM) 评估框架. 洞察整合方法与专家评估密切匹配,显示出临床验证的前景.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 为自动化医疗文档生成提供了潜力.
- 确保LLM生成的医疗文件的可靠性需要大量的专家投入,这阻碍了临床采用.
- 现有的评估方法可能无法完全捕捉到临床环境中LLM绩效的细微差别.
研究的目的:
- 开发和验证基于LLM的医疗文件生成评估框架.
- 在LLM评估框架内比较不同思维链 (CoT) 策略的有效性.
- 评估基于LLM的评估与专家人类判断的一致性.
主要方法:
- 开发了一个渐进的LLM评估框架,结合了三个思维链 (CoT) 策略:定性,定量-定性和洞察整合.
- 应用了框架来评估33个LLM生成的紧急部门记录在五个关键标准.
- 基于LLM的评估结果与使用统计分析的专家评估进行了相关联.
主要成果:
- 与Insight集成的CoT策略与专家评估有很强的相关性 (r = 0.680,p < .001).
- 这种方法在与专家评估保持一致方面优于定性 (r = 0.524) 和定量-定性 (r = 0.630) 策略.
- 该框架有效地捕捉了细微的评估模式,同时保持了效率.
结论:
- 基于LLM的评估框架,特别是Insight集成方法,可以有效地与医学文件的专家评估保持一致.
- 这些框架显示了作为有效工具的潜力,用于验证LLM生成的临床记录的可靠性.
- 进一步开发和应用这些框架可以促进LLM安全地融入临床工作流程.
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