Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
This addresses the risk of misdiagnosis in clinical settings by enhancing the reliability of automatic diagnostic systems, though it appears incremental as it builds on existing LLM fine-tuning approaches.
The researchers tackled the problem of diagnostic uncertainty in explainable disease diagnosis by developing ConfiDx, an uncertainty-aware large language model that identifies uncertainties and generates explanations, achieving superior diagnostic performance on real-world datasets.
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.