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基于代理的大型语言模型系统,用于从乳腺癌综合报告中提取结构化数据:双重验证研究.

Steven N Hart1, Teya S Bergamaschi2

  • 1Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55901, United States.

JAMIA open
|February 27, 2026
PubMed
概括

大型语言模型 (LLM) 显示出提取乳腺癌病理学数据的前景,但现实世界的性能落后于合成测试. 人类监督对于临床部署至关重要.

关键词:
人工智能的人工智能是人工智能.临床数据的提取.大型语言模型.自然语言处理自然语言处理.病理学报告 病理学报告

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科学领域:

  • 人工智能在医学中的应用
  • 计算病理学计算病理学
  • 在医疗保健中的自然语言处理.

背景情况:

  • 乳腺癌病理学报告包含临床决策的关键结构化数据.
  • 从病理学报告中手动提取数据是耗时且容易出现错误的.
  • 大型语言模型 (LLM) 提供了从临床文本中自动提取结构化数据的潜力.

研究的目的:

  • 开发和验证一种基于代理的LLM系统,用于从乳腺癌综合病理学报告中提取结构化数据.
  • 评估基于LLM的数据提取的合成和现实世界的验证之间的性能差距.
  • 为了比较七个领先的LLMs在这个任务上的表现.

主要方法:

  • 开发了一个基于人工智能代理的模块化框架,使用顺序专业的LLMs.
  • 美国病理学家学院 (CAP) 标准化了癌症协议,将其分为8个部分,86个子部分和229个离散领域.
  • 使用合成 (864个案例) 和真实世界 (90个报告,6651个字段) 数据集验证了七个LLM.

主要成果:

  • 合成验证显示了高精度 (93.8%-99.0%).
  • 现实世界评估显示,业绩大幅下降 (回顾:61.8%-87.7%),表明存在"现实差距".
  • 双子-2.5-pro实现了最高的现实世界召回率 (87.7%);较小的模型 (例如,14B参数Deepseek-R1) 的表现具有竞争力.

结论:

  • 合成验证本身就提供了误导性的高可靠性.
  • 现实世界的性能恶化突显了临床文档的复杂性.
  • 由于性能差距,强制性人体验证是必不可少的;LLM作为选工具是最好的.