MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation.

Authors

Yi Lin, Yihao Ding, Yonghui Wu, Yifan Peng

Published in

Proceedings of the conference. Association for Computational Linguistics. Meeting. Volume 2026. Issue v2 / short. Pages 273-285.

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 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.

PMID:
42434711
Bibliographic data and abstract were imported from PubMed on 11 Jul 2026.

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