多智能体AI系统自动整理放射科报告并质检，本地部署保障隐私。
Multi-Agent AI System for Radiology Report Structuring and Quality Assurance with Independent Radiologist Evaluation
- 用规则+本地大模型按解剖部位拆分报告句子
- 检出90份（14.1%）报告存在内容不一致或关键发现未记录
- 医生评估显示96%重构准确，适合医院自主部署提升报告质量
目的：开发并评估一个本地部署的多智能体AI系统，用于放射科报告的结构化与质量保证。方法：本回顾性研究纳入2023至2024年15名注册放射科医生出具的638份胸部、腹部和盆腔CT检查报告。构建多智能体AI流程，实现报告结构化与质量检查。系统利用正则表达式与本地大语言模型，在句子层面将报告划分为预定义的解剖结构部分，并检测发现与结论段落间的不一致、段内矛盾、性别-解剖冲突以及重要发现未告知的情况。两名注册放射科医生独立评估了其中45份报告。结果：该系统成功将全部报告（22,270个句子）的发现部分结构化为标准解剖格式，且保留原文内容。系统标记出90份（14.1%）报告存在问题，其中最常见为段落间不一致（80份，12.5%）。在医生评估中，两位评审者一致认为31份（69%）重构正确，2份（4%）错误，其余12份（27%）存在分歧。双方均确认无临床重要信息丢失，也未引入虚构内容。整体质量评估中，84%的报告被评为“优秀”或“良好”，其余为“一般”。结论：该本地部署的多智能体系统在单一工作流中整合了报告结构化与质量保证功能。医生评估显示其表现良好，此类系统可助力放射科报告标准化与质量控制。
原文摘要 · Abstract (English)
Purpose: To develop and evaluate a locally deployed multi-agent AI system for radiology report structuring and quality assurance. Materials and Methods: This retrospective study included 638 radiology reports from CT examinations of the chest, abdomen, and pelvis dictated by 15 board-certified radiologists in 2023 and 2024. A multi-agent AI pipeline was developed to perform report structuring and quality assurance (QA). The system structured the report into standardized anatomical sections at the sentence level using regex rules and local large language models. It also detected mismatches between the Findings and Impression sections, or within sections; gender-anatomy conflicts; and undocumented communication of critical findings. Two board-certified radiologists independently evaluated a 45-report subset. Results: The multi-agent system structured the Findings sections of all reports (22,270 sentences) into a predefined anatomical format while retaining the original report content. The system flagged 90 (14.1%) reports, most commonly for section mismatches (80 reports, 12.5%). In the radiologist evaluation, both reviewers agreed that 31 (69%) were correctly restructured, 2 reports (4%) were incorrectly restructured, and disagreed on the remaining 12 reports (27%). Both reviewers agreed that no clinically important information was omitted and no fabricated content was introduced. Overall QA performance was rated as "excellent" or "good" in 84% of the evaluated reports, with the remaining reports rated as "fair". Conclusion: A locally deployed multi-agent AI system combined radiology report structuring and quality assurance within a single workflow. The system demonstrated favorable performance in radiologist evaluation. Such systems may support standardization of reporting and quality assurance in radiology practice.
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