算法监管,从药物监管中吸取的教训
Alan Balendran1, Mehdi Benchoufi2, Theodoros Evgeniou3
1Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France. alan.balendran@u-paris.fr.
NPJ digital medicine
|October 2, 2024
概括
医疗保健中的人工智能 (AI) 系统需要严格的监控. 调整药监方法可以提高AI的安全性,并减轻部署后事件的风险.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 人工智能 (AI) 系统越来越多地用于医疗保健等高风险行业.
- 尽管有评估工作,但人工智能系统在部署后遇到事件,这给缓解带来了重大挑战.
- 现有的药物安全框架,称为药物监测,为监测现实世界的表现提供了先例.
研究的目的:
- 探索药物监测原则的适应,以监测医疗保健中的AI系统.
- 加强应对与人工智能部署相关的不利影响和风险.
- 为改善AI在医疗保健和其他领域的安全协议提供基础.
主要方法:
- 药物监督原则的概念性调整.
- 关于人工智能安全和药物安全监测的文献综述.
- 讨论人工智能系统监控的潜在框架.
主要成果:
- 药监监测为人工智能系统监控提供了一个可转移的模型.
- 诸如不良事件检测和风险评估等关键概念可以应用于AI.
- 提出了一种结构化的方法来管理与AI相关的事件.
结论:
- 调整药监是确保医疗保健中人工智能安全的一个有希望的策略.
- 这种方法可以更有效地识别和减轻AI相关风险.
- 拟议的框架对超越医疗保健部门的人工智能安全有影响.
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