MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910259698728960 |
|---|---|
| author | Zhan, Yuqi Wu, Xinyue Lin, Tianyu Bao, Yutong Wang, Xiaoyu Cheng, Weihao Chen, Huangwei Qin, Feiwei Zhu, Zhu |
| author_facet | Zhan, Yuqi Wu, Xinyue Lin, Tianyu Bao, Yutong Wang, Xiaoyu Cheng, Weihao Chen, Huangwei Qin, Feiwei Zhu, Zhu |
| contents | Large language models (LLMs) have shown promise in clinical diagnosis but remain limited by unreliable report generation, weak evidence grounding, and opaque reasoning. We propose MedCollab, an IBIS-guided multi-agent framework for full-cycle clinical diagnosis and diagnostic report generation. Mimicking hospital consultation, MedCollab dynamically recruits specialist and exam agents from patient records. Each diagnostic hypothesis is structured through the Issue-Based Information System (IBIS) into evidence-linked arguments, improving traceability and auditability. MedCollab further constructs Hierarchical Disease Relation Chains (HDRC) to organize accepted hypotheses into clinically meaningful pathological and comorbidity relations. A verifier-guided consensus module audits reasoning quality, detects contradictions, and updates agent weights over multiple rounds. Experiments on ClinicalBench and MIMIC-IV show that MedCollab outperforms strong LLM and medical multi-agent baselines in diagnostic accuracy, department routing, evidence consistency, and report quality. These results demonstrate that structured argumentation and disease-relation modeling can improve the reliability, transparency, and clinical coherence of LLM-based diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01131 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis Zhan, Yuqi Wu, Xinyue Lin, Tianyu Bao, Yutong Wang, Xiaoyu Cheng, Weihao Chen, Huangwei Qin, Feiwei Zhu, Zhu Multiagent Systems Artificial Intelligence Large language models (LLMs) have shown promise in clinical diagnosis but remain limited by unreliable report generation, weak evidence grounding, and opaque reasoning. We propose MedCollab, an IBIS-guided multi-agent framework for full-cycle clinical diagnosis and diagnostic report generation. Mimicking hospital consultation, MedCollab dynamically recruits specialist and exam agents from patient records. Each diagnostic hypothesis is structured through the Issue-Based Information System (IBIS) into evidence-linked arguments, improving traceability and auditability. MedCollab further constructs Hierarchical Disease Relation Chains (HDRC) to organize accepted hypotheses into clinically meaningful pathological and comorbidity relations. A verifier-guided consensus module audits reasoning quality, detects contradictions, and updates agent weights over multiple rounds. Experiments on ClinicalBench and MIMIC-IV show that MedCollab outperforms strong LLM and medical multi-agent baselines in diagnostic accuracy, department routing, evidence consistency, and report quality. These results demonstrate that structured argumentation and disease-relation modeling can improve the reliability, transparency, and clinical coherence of LLM-based diagnosis. |
| title | MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2603.01131 |