Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation: Radiologist-Like Workflow with Clinically Verifiable Rewards

• URL: http://arxiv.org/abs/2603.16876v1
• Date: Tue, 17 Feb 2026 12:48:32 GMT
• Title: Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation: Radiologist-Like Workflow with Clinically Verifiable Rewards
• Abstract summary: We propose MARL-Rad, a novel multi-modal multi-agent reinforcement learning framework for radiology report generation.<n>MarL-Rad coordinates region-specific agents and a global integrating agent, optimized via clinically verifiable rewards.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose MARL-Rad, a novel multi-modal multi-agent reinforcement learning framework for radiology report generation that coordinates region-specific agents and a global integrating agent, optimized via clinically verifiable rewards. Unlike prior single-model reinforcement learning or post-hoc agentization of independently trained models, our method jointly trains multiple agents and optimizes the entire agent system through reinforcement learning. Experiments on the MIMIC-CXR and IU X-ray datasets show that MARL-Rad consistently improves clinically efficacy (CE) metrics such as RadGraph, CheXbert, and GREEN scores, achieving state-of-the-art CE performance. Further analyses confirm that MARL-Rad enhances laterality consistency and produces more accurate, detail-informed reports.

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