让大模型主动识别诊断不确定性，提升医疗决策可信度
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
- 用诊断标准微调大模型，显式捕捉诊断不确定性
- 在真实数据集上准确识别不确定情况，诊断性能更优
- 适合临床辅助诊断系统开发，提升AI医疗可靠性
可解释疾病诊断通过整合患者信息（如症状）与计算模型，生成可能的诊断及推理过程，具有明确临床价值。然而，当临床记录缺乏充分证据时（如缺乏确定性症状），常出现诊断不确定性，增加误诊风险。尽管明确识别并解释不确定性对可信诊断系统至关重要，但该问题仍研究不足。为此，我们提出ConfiDx——一种基于开源大模型、经诊断标准微调的不确定性感知语言模型。我们形式化了该任务，并构建了富含标注的多级诊断模糊性数据集。在真实世界数据集上的评估表明，ConfiDx在识别诊断不确定性方面表现优异，诊断性能更佳，并能生成可信的诊断与不确定性解释。据我们所知，这是首个联合解决诊断不确定性识别与解释的研究，显著提升了自动诊断系统的可靠性。
原文摘要 · Abstract (English)
Explainable disease diagnosis, which leverages patient information (e.g., signs and symptoms) and computational models to generate probable diagnoses and reasonings, offers clear clinical values. However, when clinical notes encompass insufficient evidence for a definite diagnosis, such as the absence of definitive symptoms, diagnostic uncertainty usually arises, increasing the risk of misdiagnosis and adverse outcomes. Although explicitly identifying and explaining diagnostic uncertainties is essential for trustworthy diagnostic systems, it remains under-explored. To fill this gap, we introduce ConfiDx, an uncertainty-aware large language model (LLM) created by fine-tuning open-source LLMs with diagnostic criteria. We formalized the task and assembled richly annotated datasets that capture varying degrees of diagnostic ambiguity. Evaluating ConfiDx on real-world datasets demonstrated that it excelled in identifying diagnostic uncertainties, achieving superior diagnostic performance, and generating trustworthy explanations for diagnoses and uncertainties. To our knowledge, this is the first study to jointly address diagnostic uncertainty recognition and explanation, substantially enhancing the reliability of automatic diagnostic systems.
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