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大型语言模型与手动审查用于乳腺癌的临床数据治愈:回顾性比较研究

Young-Joon Kang¹, Hocheol Lee², Jae Pak Yi¹

¹Department of Surgery, College of Medicine, The Catholic University of Korea, Incheon St Mary's Hospital, 56, Dongsu-ro, Bupyeong-gu, Incheon, 21431, Republic of Korea, 01026383847.

JMIR medical informatics

|November 6, 2025

概括

大型语言模型 (LLMs) 在提取乳腺癌数据方面实现了90.8%的准确性,与手册审查相比,大大减少了处理时间和医生工作时间. 这种自动化方法提高了临床研究的效率.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

对于临床研究而言,手动审查电子健康记录 (EHR) 是耗时且不一致的.
大型语言模型 (LLM) 显示出数据提取自动化的前景,但它们在外科瘤学中的应用尚未得到充分研究.

研究的目的:

评估基于LLM的数据处理与从乳腺癌患者记录中提取临床信息的手动医生审查的可行性和准确性.

主要方法:

一项回顾性比较研究分析了来自五家学术医院的乳腺癌记录.
两种途径进行了比较:手动医生审查和LLM (Claude 3.5 Sonnet) 处理非识别数据.
验证涉及每组900个数据点,由四名乳腺外科瘤学家进行评估,与国家注册表数据相比,准确度高.

主要成果:

该LLM获得了90.8%的准确性,与手动审查相比.
通过LLM处理,医生工作时间减少了91% (12天而不是7个月),并捕获了更多的生存事件.
虽然LLM显示了更好的淋巴结文档,但手册审查中缺少的癌症分期数据较少.

结论:

基于LLM的自动提取,无标识数据的策划是有效的,并且比手动审查更有效.
这种两步方法平衡了数据隐私和研究效率.
法学士处理加速了回顾性临床研究,同时保持了数据质量和患者隐私.

关键词:

人工智能的人工智能是人工智能.乳腺新生体的发生.临床瘤学临床瘤学数据挖掘是数据挖掘的一个方法.大型语言模型自然语言处理自然语言处理.

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