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关于人工智能/机器学习 (AI/ML) 在晚期临床开发中的应用

Karl Köchert¹, Tim Friede^2,3, Michael Kunz⁴

¹Bayer AG, Berlin, Germany.

Therapeutic innovation & regulatory science

|August 21, 2024

概括

人工智能和机器学习 (AI/ML) 现在可用于临床开发. 本研究探讨了它们在药物开发后期阶段的作用和标准,重点关注稳定性,透明度和可追溯性.

科学领域:

临床药理学临床药理学
生物统计学生物统计学
人工智能在医学中的应用

背景情况:

人工智能和机器学习 (AI / ML) 越来越容易获得临床开发.
利益相关者需要明确AI/ML在医疗保健中的现实角色和标准.
晚期临床研究要求AI/ML应用具有高标准的稳定性,透明度和可追溯性.

研究的目的:

探索AI/ML方法在临床药物开发后期阶段的应用.
总结现有的监管指导和统计工作,这些工作与AI/ML相关.
激发关于AI/ML分析在药物开发中的一般作用的讨论.

主要方法:

审查当前的监管指南和关于AI/ML在临床开发中的统计文献.
介绍一项行业案例研究,应用ML方法来调查基线特征对治疗效果的影响.
使用标准化的ML方法与可解释AI (XAI) 进行直观的图形显示.

主要成果:

证明ML方法在临床试验后期阶段分析广泛的基线特征的能力.
成功应用标准化,可解释的AI方法,用于直观的数据可视化.
识别AI/ML的潜力,以提高对药物开发中的治疗效果的理解.

结论:

在临床药物开发的后期阶段,AI/ML方法可以得到强大而透明的应用.
标准化的方法和可解释的AI对于将AI/ML整合到临床研究中至关重要.
需要进一步讨论,以确定AI/ML在药物开发中的最佳作用和标准.

关键词:

探索性数据分析探索性数据分析机器学习机器学习第三阶段临床试验制定监管决策制定监管决策

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