Algorithmovigilance, lessons from pharmacovigilance

被引：3
作者：
Balendran, Alan [1 ,2 ]
Benchoufi, Mehdi [1 ,2 ]
Evgeniou, Theodoros [3 ]
Ravaud, Philippe [1 ,2 ,4 ,5 ]
机构：
[1] Univ Paris Cite, Paris, France
[2] Univ Sorbonne Paris Nord, Ctr Res Epidemiol & Stat CRESS, Inserm, INRAE, Paris, France
[3] INSEAD, Fontainebleau, France
[4] Hop Hotel Dieu, Ctr Epidemiol Clin, AP HP, Paris, France
[5] Columbia Univ, Dept Epidemiol, Mailman Sch Publ Hlth, New York, NY USA
来源：
NPJ DIGITAL MEDICINE | 2024年 / 7卷 / 01期
关键词：
CAUSALITY ASSESSMENT; HEALTH;
D O I：
10.1038/s41746-024-01237-y
中图分类号：
R19 [保健组织与事业（卫生事业管理）];
学科分类号：
摘要：
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.
引用
收藏
页数：6
相关论文
共 49 条
  • [41] Presenting machine learning model information to clinical end users with model facts labels
    Sendak, Mark P.
    Gao, Michael
    Brajer, Nathan
    Balu, Suresh
    [J]. NPJ DIGITAL MEDICINE, 2020, 3 (01)
  • [42] How the US drug safety system should be changed
    Strom, BL
    [J]. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2006, 295 (17): : 2072 - 2075
  • [43] MLOps: A Taxonomy and a Methodology
    Testi, Matteo
    Ballabio, Matteo
    Frontoni, Emanuele
    Iannello, Giulio
    Moccia, Sara
    Soda, Paolo
    Vessio, Gennaro
    [J]. IEEE ACCESS, 2022, 10 : 63606 - 63618
  • [44] U.S. Food & Drug Administration (FDA), 2020, Postmarketing Surveillance Programs
  • [45] U.S. Food & Drug Administration (FDA), 2024, FDA Dashboards-Recalls
  • [46] U.S. Food Drug Administration (FDA), 2019, Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)based software as a medical device (SaMD)discussion paper and request for feedback
  • [47] Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition
    Winkler, Julia K.
    Fink, Christine
    Toberer, Ferdinand
    Enk, Alexander
    Deinlein, Teresa
    Hofmann-Wellenhof, Rainer
    Thomas, Luc
    Lallas, Aimilios
    Blum, Andreas
    Stolz, Wilhelm
    Haenssle, Holger A.
    [J]. JAMA DERMATOLOGY, 2019, 155 (10) : 1135 - 1141
  • [48] How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals
    Wu, Eric
    Wu, Kevin
    Daneshjou, Roxana
    Ouyang, David
    Ho, Daniel E.
    Zou, James
    [J]. NATURE MEDICINE, 2021, 27 (04) : 582 - 584
  • [49] Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
    Zhang, Jiawei
    Wang, Yang
    Molino, Piero
    Li, Lezhi
    Ebert, David S.
    [J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2019, 25 (01) : 364 - 373
12345