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临床研究文件生成的大型语言模型的合规性和事实性

Zifeng Wang¹, Junyi Gao², Benjamin Danek¹

¹Keiji AI, Seattle, WA, 98115, United States.

Journal of the American Medical Informatics Association : JAMIA

|October 27, 2025

概括

大型语言模型 (LLM) 在起草临床研究文件时存在局限性. 一个新的基准和管道,InformGen,显著提高了知情同意表格的LLM合规性和事实准确性.

科学领域:

人工智能在医学中的应用
临床研究信息学临床研究信息学

背景情况:

大型语言模型 (LLM) 在高风险,符合性驱动的环境 (如临床研究文档起草) 中的性能未得到充分探索.
在临床试验中,确保患者面临的文件符合法规和事实准确性至关重要.

研究的目的:

建立一个基准和评估框架,以评估LLM在从临床试验协议中生成知情同意表格 (ICF) 的合规性和事实性.
开发和评估一个系统,以提高LLM在这个领域的表现.

主要方法:

介绍InformBench,这是900份临床试验文件的基准.
基于监管准则和同意模板的评估框架的开发.
评估LLM在将冗长的协议转化为简洁的ICF方面的表现.
设计InformGen,一个检索增强的人在循环管道,以提高生成质量.

主要成果:

基线LLM (例如GPT-4o) 显示了70%-80%的合规性和18%-43%的实际错误.
InformGen的管道实现了近100%的监管合规性和90%以上的事实准确性.
结果由5位领域专家验证.

结论:

目前的LLM在临床研究文件的监管敏感性和事实依据方面存在重大限制.
特定领域的基准和结构化评估对于在临床研究中安全部署LLM是必要的.
法律法学可以是有价值的,但需要适应和严格的评估,高风险的应用.

关键词:

临床试验协议临床试验协议临床试验是指临床试验中的临床试验.信息同意表格信息同意表格大型语言模型

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