BLUEmed: Retrieval-Augmented Multi-Agent Debate for Clinical Error Detection

2026-04-11

AI Summary

This work addresses the challenge of detecting clinically erroneous yet semantically plausible term substitutions in clinical notes by proposing a novel approach that integrates multi-agent structured debate with hybrid retrieval-augmented generation (RAG). The method decomposes clinical notes, retrieves evidence from multiple sources, and employs expert agents for independent analysis; when disagreements arise, it invokes structured rebuttals and cross-source adjudication, complemented by cascaded safety layers to suppress false positives. To our knowledge, this is the first framework to synergistically combine multi-agent debate with hybrid RAG—incorporating dense, sparse, and online retrieval—for clinical error detection, enabling multi-perspective validation and conflict resolution. Evaluated under few-shot settings, the model achieves 69.13% accuracy, 74.45% ROC-AUC, and 72.44% PR-AUC, substantially outperforming single-agent RAG and pure debate baselines.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Terminology substitution errors in clinical notes, where one medical term is replaced by a linguistically valid but clinically different term, pose a persistent challenge for automated error detection in healthcare. We introduce BLUEmed, a multi-agent debate framework augmented with hybrid Retrieval-Augmented Generation (RAG) that combines evidence-grounded reasoning with multi-perspective verification for clinical error detection. BLUEmed decomposes each clinical note into focused sub-queries, retrieves source-partitioned evidence through dense, sparse, and online retrieval, and assigns two domain expert agents distinct knowledge bases to produce independent analyses; when the experts disagree, a structured counter-argumentation round and cross-source adjudication resolve the conflict, followed by a cascading safety layer that filters common false-positive patterns. We evaluate BLUEmed on a clinical terminology substitution detection benchmark under both zero-shot and few-shot prompting with multiple backbone models spanning proprietary and open-source families. Experimental results show that BLUEmed achieves the best accuracy (69.13%), ROC-AUC (74.45%), and PR-AUC (72.44%) under few-shot prompting, outperforming both single-agent RAG and debate-only baselines. Further analyses across six backbone models and two prompting strategies confirm that retrieval augmentation and structured debate are complementary, and that the framework benefits most from models with sufficient instruction-following and clinical language understanding.

Problem

• clinical error detection
• terminology substitution
• medical terminology
• healthcare NLP
• automated error detection

Innovation

• Retrieval-Augmented Generation
• Multi-Agent Debate
• Clinical Error Detection
• Terminology Substitution
• Hybrid Retrieval