可解释的差异诊断与双推理大语言模型
Shuang Zhou1, Mingquan Lin1, Sirui Ding2
1Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN USA.
npj health systems
|April 28, 2025
概括
这项研究引入了一个新的数据集和框架,Dual-Inf,以改进大型语言模型 (LLM) 如何生成差异诊断 (DDx) 解释,增强临床决策.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 自动差异诊断 (DDx) 对于患者护理至关重要.
- 大型语言模型 (LLM) 显示出诊断潜力,但难以提供高质量的DDx解释.
- 缺乏专门的数据集和LLM推理挑战阻碍了DDx解释的发展.
研究的目的:
- 开发第一个公开可用的DDx数据集,并提供专家提供的解释.
- 提出和评估一种新的框架 (双Inf) 来生成基于LLM的精确DDx解释.
- 提高临床差异诊断中LLM的解释性.
主要方法:
- 创建一个新的DDx数据集,包含570个专家注释的临床笔记.
- 开发双Inf框架,从LLMs中获得高质量的DDx解释.
- 对于差异诊断的LLM可解释性的全面评估.
主要成果:
- 第一个专门的DDx解释数据集现在公开了.
- 双Inf框架在生成精确的DDx解释方面表现出有效性.
- 这项工作代表了基于LLM的临床可解释性的重大进展.
结论:
- 弥合了差异诊断解释生成中的关键差距.
- 提高了LLM提供清晰准确的DDx解释的能力.
- 旨在通过更好的AI驱动的诊断推理来改善临床决策.
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