Balendran Alan, Benchoufi Mehdi, Evgeniou Theodoros, Ravaud Philippe
Université Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France.
INSEAD, Fontainebleau, France.
NPJ Digit Med. 2024 Oct 2;7(1):270. doi: 10.1038/s41746-024-01237-y.
Artificial Intelligence (AI) systems are increasingly being deployed across various high-risk applications, especially in healthcare. Despite significant attention to evaluating these systems, post-deployment incidents are not uncommon, and effective mitigation strategies remain challenging. Drug safety has a well-established history of assessing, monitoring, understanding, and preventing adverse effects in real-world usage, known as pharmacovigilance. Drawing inspiration from pharmacovigilance methods, we discuss concepts that can be adapted for monitoring AI systems in healthcare. This discussion aims to improve responses to adverse effects and potential incidents and risks associated with AI deployment in healthcare but also beyond.
人工智能（AI）系统越来越多地被部署到各种高风险应用中，尤其是在医疗保健领域。尽管对评估这些系统给予了极大关注，但部署后出现的问题并不罕见，有效的缓解策略仍然具有挑战性。药物安全在评估、监测、理解和预防实际使用中的不良反应方面有着悠久的历史，这一过程被称为药物警戒。我们从药物警戒方法中汲取灵感，讨论可适用于监测医疗保健领域人工智能系统的概念。本次讨论旨在改善对与医疗保健领域人工智能部署相关的不良反应、潜在事件和风险的应对措施，而且不仅限于此。
