# A Multimodal Multi-Agent Framework for Radiology Report Generation

arXiv: https://arxiv.org/abs/2505.09787 · HTML: https://mlsanity.com/paper/2505.09787/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 43 of its 62 references that are in the map.
- First posted on arXiv 14 May 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [A Survey on Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2402.00253/) (2024)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [What Makes Multi-modal Learning Better than Single (Provably)](https://mlsanity.com/paper/2106.04538/) (2021)
- [Investigating the Catastrophic Forgetting in Multimodal Large Language Models](https://mlsanity.com/paper/2309.10313/) (2023)
- [R2GenGPT: Radiology Report Generation with Frozen LLMs](https://mlsanity.com/paper/2309.09812/) (2023)
- [MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models](https://mlsanity.com/paper/2410.13085/) (2024)
- [MAIRA-1: A specialised large multimodal model for radiology report generation](https://mlsanity.com/paper/2311.13668/) (2023)
- [ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM](https://mlsanity.com/paper/2408.12076/) (2024)
- [MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding](https://mlsanity.com/paper/2503.13964/) (2025)
- [CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models](https://mlsanity.com/paper/2406.06007/) (2024)
- [RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models](https://mlsanity.com/paper/2407.05131/) (2024)
- [Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology](https://mlsanity.com/paper/2402.14252/) (2024)
- [Radiology-Llama2: Best-in-Class Large Language Model for Radiology](https://mlsanity.com/paper/2309.06419/) (2023)
- [MEDCO: Medical Education Copilots Based on A Multi-Agent Framework](https://mlsanity.com/paper/2408.12496/) (2024)
- [RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training](https://mlsanity.com/paper/2303.00534/) (2023)
- [Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation](https://mlsanity.com/paper/2408.00555/) (2024)
- [Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models](https://mlsanity.com/paper/2308.16463/) (2023)
- [Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models](https://mlsanity.com/paper/2305.03660/) (2023)
- [Fact-Aware Multimodal Retrieval Augmentation for Accurate Medical Radiology Report Generation](https://mlsanity.com/paper/2407.15268/) (2024)

## Built on by (most cited first, 8 of 8)

- [MAS2: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems](https://mlsanity.com/paper/2509.24323/) (2025)
- [Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives](https://mlsanity.com/paper/2510.24551/) (2025)
- [Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation](https://mlsanity.com/paper/2603.16876/) (2026)
- [CogRad: A Cognitively-Inspired Multi-Agent Framework for Radiology Report Generation](https://mlsanity.com/paper/2607.03853/) (2026)
- [Route, Retrieve, Reflect, Repair: Self-Improving Agentic Framework for Visual Detection and Linguistic Reasoning in Medical Imaging](https://mlsanity.com/paper/2601.08192/) (2026)
- [MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction](https://mlsanity.com/paper/2606.25651/) (2026)
- [Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation](https://mlsanity.com/paper/2607.17341/) (2026)
- [A Modular Agent for Reliable and Auditable Spatial Relation Verification in CT Scans](https://mlsanity.com/paper/2608.21140/) (2026)

## Abstract (arXiv)

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.