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关于使用大型语言模型进行研究的TRIPOD-LLM报告准则.

Jack Gallifant^1,2,3, Majid Afshar⁴, Saleem Ameen^1,5,6

¹Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.

Nature medicine

|January 8, 2025

概括

新的个人预后或诊断-大型语言模型 (TRIPOD-LLM) 多变量模型的透明报告指南加强了医疗保健中大型语言模型的报告. 这些指导方针旨在提高LLM研究的质量和临床使用.

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科学领域:

生物医学信息学生物医学信息学
医疗保健中的人工智能
临床研究方法论临床研究方法论

背景情况:

大型语言模型 (LLM) 在医疗保健中越来越多地使用,但缺乏标准化的报告准则.
现有的报告标准需要调整,以应对生物医学应用中LLM所带来的独特挑战.

研究的目的:

引入TRIPOD-LLM (个人预后或诊断-大型语言模型的多变量模型的透明报告),这是TRIPOD+AI的延伸.
为提供全面的检查清单,以透明地报告医疗保健中的基于LLM的研究.

主要方法:

通过加快的德尔菲进程和专家共识,开发TRIPOD-LLM.
创建一个模块化检查列表,包含19个主要项目和50个子项目,可适应各种LLM研究设计.
引入一个交互式网站,用于完成指南和生成PDF.

主要成果:

特里波德-LLM提供了一个详细的框架,涵盖了从标题到讨论的所有研究方面.
该指南强调了LLM的透明度,人类监督和特定任务的绩效报告.
模块化格式可确保在各种LLM研究任务和设计中适用.

结论:

三角法学士 (TRIPOD-LLM) 提供了必要的指导方针,以提高法学士研究在医疗保健中的质量,可复制性和临床适用性.
这些指导方针作为一个活着的文件,准备随着该领域的进步而发展.
通过TRIPOD-LLM进行标准化报告对于在临床实践中负责任地采用LLM至关重要.