使用大型语言模型来确定监视结肠镜间隔:一个双机构验证研究
Vedant Acharya1, Shivan J Mehta2, Daniel A Sussman3
1Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
The American journal of gastroenterology
|December 6, 2025
概括
大型语言模型 (LLM) 准确地使用美国多社会工作组的指导方针来确定多斑管切除术后结肠镜监测间隔. 这种自动化方法提高了对监控建议的遵守.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 确定多斑切除术后结肠镜监测间隔需要合成复杂的指导算法.
- 手动应用这些指南,例如美国多社会工作组 (USMSTF) 的建议,容易出现错误.
- 需要自动化工具,包括大型语言模型 (LLM),以改善遵守监控指南.
研究的目的:
- 评估大型语言模型 (LLM) 的性能,以确定符合指导方针的多角切除术后监测间隔.
- 在1000份结肠镜和病理学报告的真实数据集上评估LLM的准确性.
主要方法:
- 分析了从2023年到2024年的1000份结肠镜检查和病理学报告.
- 用GPT-4o LLM,一个自定义提示符详细说明USMSTF监控算法,来确定监控间隔.
- 为了确保可靠性,该实验重复了10次.
主要成果:
- 在10次实验中,LLM的平均准确率为94.6%.
- 准确度很高,不管是源机构还是上部肠道内镜数据的存在.
- 精度为95.8%的病例有1-3个息肉,而4个+息肉的病例为88.2% (p<0.001).
结论:
- 大型语言模型 (LLM) 在应用多叶切除术后监测指南方面表现出高准确性.
- 定制提示的LLM为确定结肠镜监测间隔提供了可靠的自动化解决方案.
- 这项技术有可能在临床实践中显著改善准则的遵守.
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