Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis
Researchers have developed a novel neuro-symbolic framework that integrates large language models (LLMs) with formal logic for more explainable and verifiable disease diagnosis. This system embeds patient narratives and clinical guidelines into a knowledge base, allowing LLMs to extract structured medical information. The extracted data is then processed through a two-stage reasoning process involving symbolic generalization and logic programming to derive auditable diagnostic conclusions. AI
IMPACT This framework offers a path towards more trustworthy medical AI by providing interpretable reasoning chains for diagnoses.