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

Yi Lin¹, Yihao Ding², Yonghui Wu³

¹Weill Cornell Medicine, New York, USA.

Proceedings of the Conference. Association for Computational Linguistics. Meeting

|July 11, 2026

Summary

This summary is machine-generated.

We developed MARCH, a multi-agent AI framework for radiology report generation. It mimics clinical hierarchy to improve accuracy and reduce AI hallucinations in medical imaging.

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Area of Science:

Artificial Intelligence
Medical Imaging
Radiology

Background:

Automated 3D radiology report generation faces challenges with clinical hallucinations and lacks iterative verification.
Current Vision-Language Models (VLMs) function as black-box systems, omitting collaborative oversight crucial in clinical workflows.

Purpose of the Study:

To introduce MARCH (Multi-Agent Radiology Clinical Hierarchy), a novel multi-agent framework designed to emulate a radiology department's hierarchy for improved report generation.
To enhance the reliability and clinical fidelity of AI-generated radiology reports by incorporating specialized agent roles and iterative consensus.

Main Methods:

MARCH employs a hierarchical structure with specialized agents: a Resident Agent for initial drafting using multi-scale CT feature extraction.
Fellow Agents conduct retrieval-augmented revisions, while an Attending Agent facilitates consensus discourse to resolve discrepancies.
The framework simulates a collaborative clinical workflow to ensure accuracy and reduce errors.

Main Results:

MARCH significantly outperformed state-of-the-art baselines on the RadGenome-ChestCT dataset.
The framework demonstrated superior clinical fidelity and linguistic accuracy in automated radiology report generation.
Results indicate improved reliability of AI in high-stakes medical domains through hierarchical, multi-agent collaboration.

Conclusions:

Modeling human-like organizational structures, such as clinical hierarchies, enhances the reliability of AI in medical applications.
MARCH offers a promising approach to mitigate hallucinations and improve the accuracy of AI-generated radiology reports.
The multi-agent, collaborative framework represents a significant advancement in clinical AI systems.