Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
Journal:
arXiv
Published Date:
May 6, 2025
Abstract
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.
