Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis

Clinical decision-making requires reasoning over incomplete, imprecise, and linguistically expressed patient narratives. While large language models (LLMs) excel at extracting latent information from natural language, they lack the verifiability and interpretability essential for trustworthy medical AI. We propose a neuro-symbolic reasoning framework that aligns LLMs with formal logic to enable explainable and formally verifiable medical diagnosis. Patient descriptions and clinical guidelines ar

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ethics.ai (25 May 2026), “Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis,” evidence record 3748, https://ethics.ai/record/3748 (originally published by arXiv).

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