EviAgent让放射科报告生成可追溯，靠视觉证据和外部知识提升可信度。
EviAgent: Evidence-Driven Agent for Radiology Report Generation
- 分步拆解生成流程，引入多维视觉专家与检索模块增强透明性。
- 在MIMIC-CXR等3个数据集上超越通用与专用模型，报告更准确可信。
- 适合需要可解释性医疗AI的临床场景，提升医生信任度。
自动化放射科报告生成具有缓解放射科医生工作负担的巨大潜力。尽管近期多模态大模型具备强大的视觉-语言能力，但其临床应用受限于固有缺陷：决策过程如‘黑箱’，生成报告缺乏显式视觉证据支持诊断，且难以获取外部领域知识。为此，我们提出证据驱动的放射科报告生成代理（EviAgent）。不同于不透明的端到端范式，EviAgent通过将复杂生成过程分解为细粒度操作单元，实现透明的推理路径。系统集成多维度视觉专家与检索机制作为外部支持模块，赋予模型显式视觉证据与高质量临床先验。在MIMIC-CXR、CheXpert Plus和IU-Xray三个数据集上的大量实验表明，EviAgent优于大规模通用模型与专用医学模型，为自动化放射科报告生成提供了一个稳健且可信的解决方案。
原文摘要 · Abstract (English)
Automated radiology report generation holds immense potential to alleviate the heavy workload of radiologists. Despite the formidable vision-language capabilities of recent Multimodal Large Language Models (MLLMs), their clinical deployment is severely constrained by inherent limitations: their "black-box" decision-making renders the generated reports untraceable due to the lack of explicit visual evidence to support the diagnosis, and they struggle to access external domain knowledge. To address these challenges, we propose the Evidence-driven Radiology Report Generation Agent (EviAgent). Unlike opaque end-to-end paradigms, EviAgent coordinates a transparent reasoning trajectory by breaking down the complex generation process into granular operational units. We integrate multi-dimensional visual experts and retrieval mechanisms as external support modules, endowing the system with explicit visual evidence and high-quality clinical priors. Extensive experiments on MIMIC-CXR, CheXpert Plus, and IU-Xray datasets demonstrate that EviAgent outperforms both large-scale generalist models and specialized medical models, providing a robust and trustworthy solution for automated radiology report generation.
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