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为大型语言模型 (LLM) 辅助的临床试验和预测模型构建安全和透明的工作流程:技术报告

João Frutuoso^1,2

¹Critical Care, Unidade Local de Saúde Lisboa Ocidental, Lisbon, PRT.

|October 20, 2025

概括

本报告介绍了七步工作流程,以安全地将大型语言模型 (LLM) 集成到临床研究中. 它通过将治理,技术保障和人工智能在试验中的人类监督相结合,确保了科学完整性和公众信任.

科学领域:

临床研究和人工智能
医学预测建模医学预测建模
负责任的人工智能实施

背景情况:

大型语言模型 (LLM) 在临床试验和预测模型中越来越多地被使用,在显著的担忧之外,它呈现出效率的提高.
关键的挑战包括在人工智能驱动的研究中确保隐私,公平,准确和问责.
在LLM采用过程中,保持科学标准和公众信任至关重要.

研究的目的:

为研究团队提出一个结构化,七步的工作流程,以负责任地采用LLM.
为国际AI报告准则提供可重复使用的检查清单和地图研究类型.
为了减轻与LLM使用相关的风险,例如数据偏差和伪造信息.

主要方法:

一个七步的工作流程,包括范围定义,文献审查,模型评估,文档,质量门,披露和安全.
制定与报告准则相一致的检查清单,例如CONSORT-AI,SPIRIT-AI,TRIPOD+AI,PRISMA和DECIDE-AI. 这些都是报告准则.
整合治理规则,技术保障和人类监督.

主要成果:

一个实用和可审计的框架,用于将LLMs整合到临床试验和预测模型中.
对风险的缓解策略包括伪造的引用,有偏见的输出和过度依赖人工智能.
强调在研究中增强,而不是取代人类推理.

结论:

拟议的工作流程使LLMs的整合成为可能,同时保持科学严谨性和公众信任.
它平衡了人工智能的速度与必要的问责制,可复制性和透明度.
这种方法促进了人工智能辅助临床研究的负责任创新.

关键词:

生成性 AI 是一种创造性 AI.大型语言模型.出版伦理学出版伦理学报告准则报告准则研究完整性研究完整性

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