MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
arXiv AIArchived Apr 20, 2026✓ Full text saved
arXiv:2604.16175v1 Announce Type: new Abstract: Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice. While recent Vision-Language Models (VLMs) have advanced the field, they typically operate as monolithic "black-box" systems without the collaborative oversight characteristic of clinical workflows. To address these challenges, we propose MARCH (Multi-Agent Radiology Clinical Hierarchy), a multi-agent
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✦ AI Summary· Claude Sonnet
Computer Science > Artificial Intelligence
[Submitted on 17 Apr 2026]
MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
Yi Lin, Yihao Ding, Yonghui Wu, Yifan Peng
Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice. While recent Vision-Language Models (VLMs) have advanced the field, they typically operate as monolithic "black-box" systems without the collaborative oversight characteristic of clinical workflows. To address these challenges, we propose MARCH (Multi-Agent Radiology Clinical Hierarchy), a multi-agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct agents. MARCH utilizes a Resident Agent for initial drafting with multi-scale CT feature extraction, multiple Fellow Agents for retrieval-augmented revision, and an Attending Agent that orchestrates an iterative, stance-based consensus discourse to resolve diagnostic discrepancies. On the RadGenome-ChestCT dataset, MARCH significantly outperforms state-of-the-art baselines in both clinical fidelity and linguistic accuracy. Our work demonstrates that modeling human-like organizational structures enhances the reliability of AI in high-stakes medical domains.
Comments: Accepted by ACL 2026 main conference
Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2604.16175 [cs.AI]
(or arXiv:2604.16175v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.16175
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From: Yi Lin [view email]
[v1] Fri, 17 Apr 2026 15:42:03 UTC (3,048 KB)
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