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A Multimodal Multi-Agent Framework for Radiology Report Generation
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A Multimodal Multi-Agent Framework for Radiology Report Generation
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Radiology report generation (RRG) aims to automatically produce diagnostic reports from medical images, with the potential to enhance clinical workflows and reduce radiologists' workload. While recent approaches leveraging multimodal large language models (MLLMs) and retrieval-augmented generation (RAG) have achieved strong results, they continue to face challenges such as factual inconsistency, hallucination, and cross-modal misalignment. We propose a multimodal multi-agent framework for RRG that aligns with the stepwise clinical reasoning workflow, where task-specific agents handle retrieval, draft generation, visual analysis, refinement, and synthesis. Experimental results demonstrate that our approach outperforms a strong baseline in both automatic metrics and LLM-based evaluations, producing more accurate, structured, and interpretable reports. This work highlights the potential of clinically aligned multi-agent frameworks to support explainable and trustworthy clinical AI applications.
Forward citations
Cited by 6 Pith papers
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MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction
MedGuards introduces a multi-agent in-context learning framework for medical error detection and correction plus the KPCS metric, reporting improvements on four multilingual clinical note datasets.
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Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation
MARL-Rad trains region-specific and global agents with reinforcement learning on clinical rewards to produce more accurate radiology reports than prior methods on MIMIC-CXR and IU X-ray datasets.
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Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation
EgoMed-Agent reports 71.34% average Dice on a new egocentric medical segmentation benchmark (523 videos, 5 modalities), versus 11.70% for zero-shot text-prompted baselines, using detector + LLM clarification + SAM2 pr...
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CogRad: A Cognitively-Inspired Multi-Agent Framework for Radiology Report Generation
A four-agent Scout–Investigator–Writer–Verifier pipeline with slot-attention regions and inference-time sentence re-examination leads NLG baselines on CheXpert Plus and IU X-Ray, with weaker clinical entity scores.
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MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction
MedGuards proposes a multi-agent system for medical error detection and correction plus the KPCS metric, reporting gains on four multilingual clinical-note datasets.
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MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
MARCH is a multi-agent system mimicking radiology department hierarchy that generates more clinically accurate and linguistically correct CT reports than prior single-model approaches.
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