为什么传统验证可能不足以实现生物分析中的人工智能:来自欧洲生物分析论坛的观点
Philip Timmerman1, Katja Zeiser2, Connor Walker3
1European Bioanalysis Forum, Brussels, Belgium.
Bioanalysis
|July 29, 2025
概括
传统的验证方法在生物分析中对人工智能 (AI) 失败. 一种新的方法,即适应性资格,强调对人工智能系统的科学监督和信任,确保患者安全和创新.
科学领域:
- 生物分析是一种生物分析.
- 人工智能的人工智能
- 监管科学 监管科学
背景情况:
- 传统的验证框架对于生物分析中的人工智能 (AI) 是不够的.
- 人工智能系统是动态的,需要不同的资格方法,而不是静态的,决定性的工具.
- 欧洲生物分析论坛2025年春季重点研讨会强调了这些挑战.
研究的目的:
- 挑战人工智能应用应该使用传统方法进行验证的假设.
- 提出适应性资格作为人工智能在生物分析中的新框架.
- 探索人工智能系统科学监督的演变.
主要方法:
- 基于研讨会讨论的概念框架开发.
- 对当前AI验证实践的局限性进行分析.
- 适应性资格原则的建议:科学监督,上下文相关性和获得的信任.
主要成果:
- 人工智能应该被视为一个学习系统,类似于一个实习生,而不是一个静态的工具.
- 监管必须超越单纯的合规,以确保透明度,稳定性和适合目的.
- 适应性资格为在生物分析中验证AI提供了一条前进的道路.
结论:
- 科学家必须领导对人工智能的验证过程的调整.
- 重点应该是以清晰和协作的方式负责任地指导创新.
- 在生物分析中不断发展的人工智能领域,保持患者的关注度至关重要.
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