Neuro-Symbolic Resolution of Recommendation Conflicts in Multimorbidity Clinical Guidelines

2026-04-19

AI Summary

This study addresses the challenge of conflicting recommendations in clinical guidelines arising from disciplinary silos, which exacerbate cognitive burden for clinicians and contribute to AI hallucinations in multimorbidity contexts. The authors propose a neurosymbolic integration framework that leverages a multi-agent system to translate unstructured guidelines into symbolic logic, followed by logical verification via a SAT solver. Introducing the novel concept of “local conflict,” the work reveals that 90.6% of conflicts originate from intersections of comorbidities. By front-loading logical validation, this approach establishes a new paradigm for knowledge coordination in medical AI. Evaluated on a benchmark of 12 SGLT2 inhibitor guidelines, the method achieves an F1 score of 0.861 in conflict detection, significantly outperforming current large language models.

Technology Category

Constraint Satisfaction and Optimization: Satisfiability Modulo TheoriesMultiagent Systems: Agreement, Argumentation & NegotiationMachine Learning: Neuro-Symbolic Learning

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• clinical guidelines
• multimorbidity
• recommendation conflicts
• logical contradiction
• AI reliability

Innovation

• Neuro-Symbolic
• Clinical Guideline Conflict
• Local Conflict
• SAT Solver
• Multimorbidity