不确定性意识的大语言模型用于可解释的疾病诊断
Shuang Zhou1, Jiashuo Wang2, Zidu Xu3
1Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA.
NPJ digital medicine
|November 18, 2025
概括
ConfiDx是一种人工智能模型,可以识别和解释医学中的诊断不确定性. 这种先进的系统提高了诊断准确度,并帮助临床医生在面对不明确的患者病例时做出更好的决策.
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
- 医疗信息学 医疗信息学
背景情况:
- 使用人工智能的可解释性疾病诊断显示出临床前景,但由于患者证据不足,与诊断不确定性作斗争.
- 人工智能系统中诊断不确定性的明确识别和解释尚未得到充分探索,增加了误诊风险.
研究的目的:
- 引入ConfiDx,一种不确定性意识的大型语言模型 (LLM),旨在解决临床环境中的诊断不确定性.
- 将不确定性意识诊断的任务正式化,并创建反映诊断模两可的注释数据集.
主要方法:
- 开发了ConfiDx,这是一个精心调整的LLM,用于不确定性意识诊断的诊断标准.
- 精选丰富的注释数据集,不同程度的诊断模两可.
- 在现实世界的临床数据集上评估ConfiDx.
主要成果:
- ConfiDx在识别诊断不确定性和改善诊断性能方面表现出色.
- 该模型为诊断和不确定性产生了可靠的解释.
- 与独立专家相比,ConfiDx辅助的专家在不确定性识别 (10.7%) 和解释 (26%) 上显著改善.
结论:
- ConfiDx有效地解决了人工智能驱动的诊断系统中诊断不确定性的挑战.
- 该模型提高了人工智能产生的诊断的可信性和可解释性.
- 通过帮助专家识别和解释不确定性,ConfiDx具有改善临床决策的巨大潜力.
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