MedCollab：IBIS 引导的多智能体协作与分层疾病关系链用于临床诊断

Computer Science > Multiagent Systems arXiv:2603.01131 (cs) [Submitted on 1 Mar 2026 (v1), last revised 11 Sep 2026 (this version, v4)] Title:MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis Authors:Yuqi Zhan, Xinyue Wu, Tianyu Lin, Yutong Bao, Xiaoyu Wang, Weihao Cheng, Huangwei Chen, Feiwei Qin, Zhu Zhu View a PDF of the paper titled MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis, by Yuqi Zhan and Xinyue Wu and Tianyu Lin and Yutong Bao and Xiaoyu Wang and Weihao Cheng and Huangwei Chen and Feiwei Qin and Zhu Zhu View PDF HTML (experimental) Abstract:Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions. Large language models have advanced medical text understanding and generation. Yet their clinical use remains limited by weak evidence grounding, opaque reasoning, and inconsistent links among differential diagnosis, final diagnosis, diagnostic basis, and treatment planning. We introduce MedCollab, a multi-agent framework for full-cycle clinical diagnosis and report generation. MedCollab coordinates specialist and examination agents according to patient records. It structures agent deliberation with an Issue-Based Information System (IBIS) protocol, so that each diagnostic position is supported by patient-specific evidence and medical knowledge. It also builds Hierarchical Disease Relation Chains (HDRC) to connect accepted hypotheses through progression, complication, and comorbidity relations. During multi-round deliberation, a verifier-guided consensus module evaluates evidence support, medical plausibility, and logical conflicts. It then adjusts agent contributions and filters unsupported reasoning. Experiments on ClinicalBench and MIMIC-IV show that MedCollab outperforms leading LLMs and medical multi-agent baselines in diagnostic accuracy, evidence consistency, and clinical reasoning quality. These results indicate that structured and auditable collaboration can produce more faithful and clinically coherent diagnostic reports. Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.01131 [cs.MA] (or arXiv:2603.01131v4 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2603.01131 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yuqi Zhan [view email] [v1] Sun, 1 Mar 2026 14:25:58 UTC (6,076 KB) [v2] Tue, 26 May 2026 16:23:05 UTC (9,684 KB) [v3] Sat, 13 Jun 2026 16:35:52 UTC (9,781 KB) [v4] Fri, 11 Sep 2026 09:54:10 UTC (6,727 KB) Full-text links: Access Paper: View a PDF of the paper titled MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis, by Yuqi Zhan and Xinyue Wu and Tianyu Lin and Yutong Bao and Xiaoyu Wang and Weihao Cheng and Huangwei Chen and Feiwei Qin and Zhu Zhu View PDF HTML (experimental) TeX Source view license Current browse context: cs.MA new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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