Uncertainty-aware large language models for explainable disease diagnosis.

ABSTRACT: Explainable disease diagnosis, which leverages patient information (e.g., symptoms) and computational models to generate probable diagnoses and reasoning, holds strong clinical promise. Yet, when clinical notes lack sufficient evidence for a definitive diagnosis, such as the absence of definitive symptoms, diagnostic uncertainty commonly arises, increasing the risk of misdiagnosis. Despite its importance, the explicit identification and explanation of diagnostic uncertainty remain under-explored in artificial intelligence-driven systems. To fill this gap, we introduce ConfiDx, an uncertainty-aware large language model fine-tuned with diagnostic criteria. We formalized the task of uncertainty-aware diagnosis and curated richly annotated datasets that reflect varying degrees of diagnostic am

SUBMITTER: Zhou S 

PROVIDER: S-EPMC12627459 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature