用大模型自动生成放射科报告模板，省去专家人工讨论
ASTAR: Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora
- 用大语言模型从大量自由文本报告中自动归纳标准化模板
- 在4215份胎儿脑部MRI报告上，效果优于两个专家制定的模板
- 将模板开发时间从数周缩短至数小时，适合医疗AI数据构建
结构化报告将放射科自由文本描述转化为可查询的数据键，有助于队列构建、长期追踪以及医学AI训练标签生成。当前主流方法采用两阶段流程：(1) 构建报告模板，(2) 提取信息填充模板。尽管信息提取已受益于大语言模型（LLMs）的发展，但模板构建仍依赖耗时的人工专家共识，存在静态、难扩展、难以反映真实报告多样性等问题。本文提出 extbf{ exttt{ASTAR}}，一种基于大语言模型的自动化框架，用于从大规模临床自由文本语料中归纳标准化放射科报告模板。在来自多个中心的4,215份胎儿脑部MRI报告上的实验表明， extbf{ exttt{ASTAR}} 自动生成的模板在模板覆盖率、信息保真度、诊断保真度和专家评估可用性方面均优于两个专家制定的模板，将模板开发时间从数周的委员会讨论缩短至数小时的自动化处理。代码开源：https://github.com/birthlab/ASTAR
原文摘要 · Abstract (English)
Structured reporting converts free-text radiology narratives into queryable data keys, facilitating cohort assembly, longitudinal tracking, and training label generation for medical AI. The prevailing paradigm follows a two-stage pipeline: (1) constructing a reporting template, (2) extracting information to populate it. While the extraction stage has benefited from advances in large language models (LLMs), template construction remains a manual bottleneck relying on labor-intensive expert consensus that is static, difficult to scale, and may fail to capture real-world reporting diversity. We address this limitation with \textbf{\texttt{ASTAR}}, an LLM-based framework for Automated induction of STAndardized radiology Reporting templates from large-scale clinical free-text corpora. Extensive experiments on 4,215 fetal brain MRI reports from multiple centers demonstrate that the \textbf{\texttt{ASTAR}}-induced template surpasses two expert-curated templates across template coverage, information fidelity, diagnostic fidelity, and expert-rated usability, reducing template development from weeks of committee deliberation to hours of automated processing. Code: https://github.com/birthlab/ASTAR
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