Neuro-Symbolic Resolution of Recommendation Conflicts in Multimorbidity Clinical Guidelines

TL;DR

This paper presents a neuro-symbolic framework that detects and verifies recommendation conflicts in multimorbidity clinical guidelines, improving AI reliability and addressing guideline fragmentation.

Contribution

The authors introduce a multi-agent neuro-symbolic system that translates clinical language into logical form and verifies conflicts using a SAT solver, a novel approach in medical AI.

Findings

90.6% of conflicts are local and arise from comorbidities

State-of-the-art LLMs fail to detect these conflicts

Our approach achieves an F1 score of 0.861 in conflict detection

Abstract

Clinical guidelines, typically developed by independent specialty societies, inherently exhibit substantial fragmentation, redundancy, and logical contradiction. These inconsistencies, particularly when applied to patients with multimorbidity, not only cause cognitive dissonance for clinicians but also introduce catastrophic noise into AI systems, rendering the standard Retrieval-Augmented Generation (RAG) system fragile and prone to hallucination. To address this fundamental reliability crisis, we introduce a Neuro-Symbolic framework that automates the detection of recommendation redundancies and conflicts. Our pipeline employs a multi-agent system to translate unstructured clinical natural language into rigorous symbolic logic language, which is then verified by a Satisfiability (SAT) solver. By formulating a hierarchical taxonomy of logical rule interactions, we identify a critical category termed Local Conflict - a decision conflict arising from the intersection of comorbidities. Evaluating our system on a curated benchmark of 12 authoritative SGLT2 inhibitor guidelines, we reveal that 90.6% of conflicts are Local, a structural complexity that single-disease guidelines fail to address. While state-of-the-art LLMs fail in detecting these conflicts, our neuro-symbolic approach achieves an F1 score of 0.861. This work demonstrates that logical verification must precede retrieval, establishing a new technical standard for automated knowledge coordination in medical AI.