算法警戒,从药物警戒中汲取经验教训

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Alan Balendran, Mehdi Benchoufi, Theodoros Evgeniou, Philippe Ravaud
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引用次数: 0

摘要

人工智能(AI)系统正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。尽管对这些系统的评估备受关注,但部署后发生事故的情况并不少见,有效的缓解策略仍具有挑战性。药物安全在评估、监测、了解和预防实际使用中的不良反应(即药物警戒)方面有着悠久的历史。从药物警戒方法中汲取灵感,我们讨论了可用于监控医疗保健领域人工智能系统的概念。这一讨论旨在改进对与医疗保健领域以及其他领域的人工智能部署相关的不良反应、潜在事故和风险的应对措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Algorithmovigilance, lessons from pharmacovigilance
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.
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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