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大型语言模型的高准确性在提供建议巴雷特食道管理使用现实世界多语言数据的概念研究的概念研究的高准确性

Yuri Gorelik^1,2, Fares Mazzawi¹, Andrawus Beany¹

¹Gastroenterology Institute, Rambam Health Care Campus, Haifa, Israel.

Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus

|February 9, 2026

概括

大型语言模型准确地为巴雷特食道 (Barrett's esophagus,BE) 提供了基于指导方针的管理建议,这是一个前发性疾病. 这些人工智能工具在BE的临床决策中显示出高准确度,与专家胃肠科医生相美.

科学领域:

胃肠病学和人工智能的人工智能

背景情况:

巴雷特食道 (Barrett's esophagus,BE) 是一种先发性疾病,需要标准化管理以预防腺癌.
对于BE管理的临床指南的遵守在胃肠科医生之间有所不同.

研究的目的:

评估和比较大型语言模型 (LLM) 在为BE管理提供基于指导方针的建议中的准确性.
对关键管理方面的专家共识进行LLM绩效评估.

主要方法:

分析了107例带有食管胃镜和病理学报告的BE患者病例.
两位专家提供了基于欧洲胃肠内镜学会指南的黄金标准建议.
两名LLM (Gemini 2.0,o3-mini-high) 在BE案例上接受了培训和测试.
精度是使用引导计算的;McNemar的测试比较了LLM的性能.

主要成果:

两种LLM在病理确认,下一步行动和内镜间隔方面都表现出高至近乎完美的准确性.
两种LLM之间没有观察到精度的显著差异.
在所有评估的管理方面,两家LLM之间达成了高度一致.

结论:

在评估现实世界BE演示和生成符合指导方针的管理建议方面,LLM表现出高度准确性.
法律法规显示了作为可靠工具的潜力,以支持在BE管理中一致的临床决策.

关键词:

巴雷特的食道.这些指导方针是指导方针.大型语言模型.

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