医疗保健中的人工智能算法:当前的食品和药物管理局法规是否足够?
Meghavi Mashar1, Shreya Chawla2, Fangyue Chen3
1University College London NHS Foundation Trust, London, United Kingdom.
JMIR AI
|June 14, 2024
概括
这项研究提出了人工智能 (AI) 和机器学习 (ML) 在医疗保健中的新监管途径. 它旨在通过在整个生命周期中调节算法来平衡创新与患者安全.
科学领域:
- 医疗保健条例 医疗保健条例
- 医疗人工智能 医疗人工智能
- 机器学习在医学中的应用
背景情况:
- 机器学习 (ML) 在医疗保健中越来越多地使用,这给监管带来了挑战.
- 目前的食品和药物管理局 (FDA) 对ML算法的规定阻碍了对临床环境的适应.
- 当ML算法支持或取代医疗专业人员时,确保患者安全至关重要.
研究的目的:
- 为医疗保健中的ML算法提出一种新的监管途径.
- 解决适应式机器学习技术现有监管框架的局限性.
- 确保安全有效地将ML纳入临床实践.
主要方法:
- 2017-2022年,关于人工智能,机器学习和医疗保健监管的文献综述.
- 建议制定一个新的监管框架,类似于医疗专业法规.
- 讨论技术和非技术实施的挑战和解决方案.
主要成果:
- 目前的法规阻止了ML算法适应现实世界的临床反.
- 拟议的基于生命周期的监管途径为适应性ML提供了解决方案.
- 确定了实施拟议途径的挑战和潜在解决方案.
结论:
- 需要一种新的监管方法来管理医疗保健中的ML算法的生命周期.
- 这种方法可以促进创新,同时保持患者的安全,质量和公平.
- 应对实施挑战对于实现ML在医疗保健中的全部潜力至关重要.
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