EviAgent：基于证据的放射学报告生成代理
EviAgent: Evidence-Driven Agent for Radiology Report Generation
AI总结：
EviAgent通过引入多维视觉专家和检索机制，解决放射学报告生成中的透明性与证据支持问题，实验表明其在多个数据集上优于通用和专用模型。
摘要：
自动化放射学报告生成具有减轻放射科医生工作负担的潜力。尽管最近的多模态大语言模型（MLLMs）在视觉-语言能力方面表现突出，但其临床部署受到固有限制：其
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
