MARCH：多智能体放射科临床层级用于CT报告生成
MARCH: Multi-Agent Radiology Clinical Hierarchy for CT Report Generation
AI总结：
MARCH通过模拟放射科专业层级，采用多智能体框架提升CT报告生成的临床准确性和语言准确性。
摘要：
自动化3D放射学报告生成常面临临床幻觉和缺乏人类实践中的迭代验证。尽管最近的视觉-语言模型（VLMs）推动了该领域的发展，但它们通常作为单一的'黑箱'系统，缺乏临床工作流程中的协作监督特征。为解决这些挑战，我们提出了MARCH（多智能体放射科临床层级），一个模拟放射科部门专业层级的多智能体框架，并为不同的智能体分配专门的角色。MARCH利用住院医师智能体进行初始草稿撰写，使用多尺度CT特征提取，多个轮转医师智能体进行检索增强的修订，以及主任医师智能体协调迭代的立场基于共识讨论以解决诊断差异。在RadGenome-ChestCT数据集上，MARCH在临床准确性和语言准确性方面显著优于最先进的基线模型。我们的工作证明了建模人类-like组织结构可以提高AI在高风险医疗领域的可靠性。
英文摘要：
Automated 3D radiology report generation often suffers from clinical hallucinations and a lack of the iterative verification found in human practice. While recent Vision-Language Models (VLMs) have advanced the field, they typically operate as monolithic "black-box" systems without the collaborative oversight characteristic of clinical workflows. To address these challenges, we propose MARCH (Multi-Agent Radiology Clinical Hierarchy), a multi-agent framework that emulates the professional hierarchy of radiology departments and assigns specialized roles to distinct agents. MARCH utilizes a Resident Agent for initial drafting with multi-scale CT feature extraction, multiple Fellow Agents for retrieval-augmented revision, and an Attending Agent that orchestrates an iterative, stance-based consensus discourse to resolve diagnostic discrepancies. On the RadGenome-ChestCT dataset, MARCH significantly outperforms state-of-the-art baselines in both clinical fidelity and linguistic accuracy. Our work demonstrates that modeling human-like organizational structures enhances the reliability of AI in high-stakes medical domains.
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。
