在药物开发中对因果AI的方法和监管考虑
Hana Lee1, Sky Qiu2, Spencer Haupert3
1Center for Drug Evaluation and Research, U.S. Food and Drug Administration (FDA), Silver Spring, MD, USA. hana.lee@fda.hhs.gov.
NPJ digital medicine
|February 27, 2026
概括
人工智能 (AI) 可以改善药物开发,但其在治疗效果的因果推断中的使用是有限的. 这篇论文探讨了AI.
科学领域:
- 药物开发和监管科学 药物开发和监管科学
- 因果推理方法论的因果推理方法.
- 人工智能应用的人工智能应用.
背景情况:
- 人工智能 (AI) 为推进药物开发过程提供了重大机会.
- 监管机构越来越多地发布有关AI在制药中的采用指南.
- 人工智能对因果推理的应用,对于理解治疗效应和告知监管决策至关重要,仍然不发达.
研究的目的:
- 审查有关AI在药物开发中的当前监管活动.
- 检查人工智能驱动的因果推理的统计方法.
- 确定关键的监管挑战,并证明AI在各种数据源和研究设计的因果推理中的价值.
主要方法:
- 审查监管机构的文件和关于人工智能的指导.
- 检查统计和机器学习技术用于因果推理.
- 人工智能应用在各种药物开发数据和研究中的案例说明.
主要成果:
- 目前有限的监管指南专门针对AI用于药物开发中的因果推理.
- 各种人工智能方法表明,从复杂的数据集中增强因果推理的潜力.
- 人工智能可以通过整合各种数据源来增强治疗效果估计,从而增加显著的价值.
结论:
- 在药物开发中,需要更清晰的监管途径来实现人工智能驱动的因果推理.
- 需要对人工智能统计方法进行进一步的研究和验证才能获得监管机构的接受.
- 人工智能对提高制药研究和监管决策中因果推理的准确性和效率具有重大承诺.
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