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Related Experiment Video

Updated: Jan 11, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Uncertainty-aware large language models for explainable disease diagnosis.

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

Summary

This summary is machine-generated.

ConfiDx, an AI model, identifies and explains diagnostic uncertainty in medicine. This advanced system improves diagnostic accuracy and aids clinicians in making better decisions when faced with unclear patient cases.

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Area of Science:

Artificial Intelligence in Medicine
Clinical Decision Support Systems
Medical Informatics

Background:

Explainable disease diagnosis using AI shows clinical promise but struggles with diagnostic uncertainty due to insufficient patient evidence.
Explicit identification and explanation of diagnostic uncertainty in AI systems are underexplored, increasing misdiagnosis risks.

Purpose of the Study:

To introduce ConfiDx, an uncertainty-aware large language model (LLM) designed to address diagnostic uncertainty in clinical settings.
To formalize the task of uncertainty-aware diagnosis and create annotated datasets reflecting diagnostic ambiguity.

Main Methods:

Developed ConfiDx, an LLM fine-tuned with diagnostic criteria for uncertainty-aware diagnosis.
Curated richly annotated datasets with varying degrees of diagnostic ambiguity.
Evaluated ConfiDx on real-world clinical datasets.

Main Results:

ConfiDx demonstrated excellence in identifying diagnostic uncertainties and improving diagnostic performance.
The model generated trustworthy explanations for diagnoses and uncertainties.
ConfiDx-assisted experts showed significant improvements in uncertainty recognition (10.7%) and explanation (26%) compared to standalone experts.

Conclusions:

ConfiDx effectively addresses the challenge of diagnostic uncertainty in AI-driven diagnostic systems.
The model enhances the trustworthiness and explainability of AI-generated diagnoses.
ConfiDx has substantial potential to improve clinical decision-making by aiding expert recognition and explanation of uncertainty.