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

• URL: http://arxiv.org/abs/2604.16175v1
• Date: Fri, 17 Apr 2026 15:42:03 GMT
• Title: MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
• Abstract summary: Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice.<n>We propose MARCH, a multi-agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct agents.<n>On the RadGenome-ChestCT dataset, MARCH significantly outperforms state-of-the-art baselines in both clinical fidelity and linguistic accuracy.
• Score: 20.858060411945438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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