AbstractArtificial 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.

摘要人工智能（AI）系统越来越多地部署在各种高风险应用程序中，特别是在医疗保健领域。尽管对评估这些系统给予了极大关注，但部署后事件并不罕见，有效的缓解策略仍然具有挑战性。

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..

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，称为药物警戒。从药物警戒方法中得到启发，我们讨论了可用于监测医疗保健中AI系统的概念。本次讨论旨在改进对医疗保健领域人工智能部署相关的不良影响、潜在事件和风险的应对措施，以及其他方面。。

IntroductionAs artificial intelligence (AI) systems are increasingly used across applications and sectors, ranging from healthcare to energy, financial, transportation, and others, the focus is increasingly shifting to the post-deployment stages of the AI lifecycle to ensure the safe use of these systems1.

引言随着人工智能（AI）系统越来越多地应用于从医疗保健到能源、金融、交通等各个应用和部门，重点越来越多地转移到AI生命周期的部署后阶段，以确保这些系统的安全使用1。

Monitoring of AI systems is becoming increasingly critical2, and new tools and practices are being developed for this purpose, such as statistical tools or so-called AI incidents databases3,4,5. Indeed, the number of AI incidents is already large and is expected to increase. For example, 10.196 AI-related incidents and hazards have been identified since 20146.

人工智能系统的监测变得越来越重要2，为此目的正在开发新的工具和实践，例如统计工具或所谓的人工智能事件数据库3,4,5。事实上，人工智能事件的数量已经很大，预计还会增加。例如，自20146年以来，已经确定了10.196起与AI相关的事件和危害。

While there is significant research on trustworthy AI7, there is only limited work on what happens after AI systems are deployed. Another limitation of AI monitoring is the lack of consideration of human or organizational factors8. For example, stakeholders like physicians who identify errors may lack effective feedback processes for reporting issues, hindering potential improvements to AI systems.However, large-scale monitoring of technologies, broadly defined, is not a new challenge.

虽然对值得信赖的AI7进行了大量研究，但在部署AI系统后会发生什么方面的工作却很有限。人工智能监测的另一个局限性是缺乏对人类或组织因素的考虑8。例如，识别错误的医生等利益相关者可能缺乏有效的反馈流程来报告问题，从而阻碍了人工智能系统的潜在改进。然而，对广泛定义的技术进行大规模监测并不是一项新挑战。

Healthcare regulators, such as the US Food and Drug Administration (FDA), use processes and practices to monitor potential negative side effects of drugs in the market despite rigorous clinical trials in their pre-approval stages9. For instance, each year, regulators and manufacturers recall, on average, more than 1300 drugs, with more than half potentially causing temporary or serious health problems10.

美国食品和药物管理局（FDA）等医疗保健监管机构使用流程和实践来监测市场上药物的潜在负面副作用，尽管在其预批准阶段进行了严格的临床试验9。例如，监管机构和制造商每年平均召回1300多种药物，其中一半以上可能导致暂时或严重的健康问题10。

According to the FDA, a recall is defined as a manufacturer’s removal or correction of a marketed product that the FDA considers to be in violation of the laws it administers11.Given the scale and potential of AI medical devices12.

据FDA称，召回被定义为制造商删除或纠正FDA认为违反其管理法律的上市产品11。鉴于人工智能医疗设备的规模和潜力12。

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欧洲药品管理局（EMA）。产品特性总结指南（SmPC）（欧洲药品管理局（EMA），2009）。Collins，G.S.等人，《TRIPOD+AI声明：使用回归或机器学习方法报告临床预测模型的更新指南》。BMJ 385，e078378（2024）。文章。

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Hanks, H., Austin, A., Kailasanath, V. & Park, S. UK NHS Pilots AI Tool Aimed at Reducing Bias in Healthcare Datasets—a Step Toward ‘algorithmovigilance’? https://technologyquotient.freshfields.com/post/102hisr/uk-nhs-pilots-ai-tool-aimed-at-reducing-bias-in-healthcare-datasets-a-step-towa (2022).Download referencesAcknowledgementsThe study was realized in the context of the @Hotel-Dieu project, which was funded by the Banque Publique d’Investissement in France.

Hanks，H.，Austin，A.，Kailasanath，V。＆Park，S。UK NHS飞行员AI工具旨在减少医疗保健数据集中的偏见-朝着“算法监管”迈出的一步？https://technologyquotient.freshfields.com/post/102hisr/uk-nhs-pilots-ai-tool-aimed-at-reducing-bias-in-healthcare-datasets-a-step-towa（2022年）。下载参考文献致谢这项研究是在@Hotel Dieu项目的背景下实现的，该项目由法国公共投资银行资助。

The funder played no role in the study design, data collection, analysis, and interpretation of data, or the writing of this manuscript. The authors thank Raphaël Porcher and Viet-Thi Tran for their feedback during the revision.Author informationAuthors and AffiliationsUniversité Paris Cité and Université Sorbonne Paris Nord, Inserm, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, FranceAlan Balendran, Mehdi Benchoufi & Philippe RavaudINSEAD, Fontainebleau, FranceTheodoros EvgeniouCentre d’Epidémiologie Clinique, AP-HP, Hôpital Hôtel-Dieu, Paris, FrancePhilippe RavaudColumbia University Mailman School of Public Health, Department of Epidemiology, New York, NY, USAPhilippe RavaudAuthorsAlan BalendranView author publicationsYou can also search for this author in.

资助者在研究设计，数据收集，分析和数据解释或撰写本手稿方面没有发挥任何作用。作者感谢Raphaël Porcher和Viet Thi Tran在修订期间的反馈。作者信息作者和附属机构巴黎Cit大学和巴黎诺德索邦大学，Inserm，INRAE，流行病学和统计研究中心（CRESS），巴黎，弗朗西兰·巴伦德兰，Mehdi Benchoufi＆Philippe RavaudINSEAD，枫丹白露，FranceHodoros EvgeniouCentre d'Epidémiologie Clinique，AP-HP，巴黎Hôpital Hôtel Dieu，FrancePhilippe RavaudColumbia University Mailman School of Public Health，Department of Epidemiology，New York，NY，USAFilippe RavaudAuthorsAlan BalendranView作者出版物也可以在中搜索此作者。

PubMed Google ScholarMehdi BenchoufiView author publicationsYou can also search for this author in

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PubMed Google ScholarTheodoros EvgeniouView author publicationsYou can also search for this author in

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PubMed Google ScholarPhilippe RavaudView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsA.B., T.E., and P.R. were involved in the conceptualization of this study. A.B. and T.E. wrote the first draft of the manuscript. A.B., T.E., M.B., and P.R. contributed to subsequent drafts and revisions of the final manuscript.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献。B、 ，T.E.和P.R.参与了这项研究的概念化。A、 B.和T.E.撰写了手稿的初稿。A、 B.，T.E.，M.B。和P.R.为最终稿件的后续草稿和修订做出了贡献。。

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Reprints and permissionsAbout this articleCite this articleBalendran, A., Benchoufi, M., Evgeniou, T. et al. Algorithmovigilance, lessons from pharmacovigilance.

Balendran，A.，Benchoufi，M.，Evgeniou，T.等人，《算法警戒，药物警戒的教训》。

npj Digit. Med. 7, 270 (2024). https://doi.org/10.1038/s41746-024-01237-yDownload citationReceived: 30 January 2024Accepted: 27 August 2024Published: 02 October 2024DOI: https://doi.org/10.1038/s41746-024-01237-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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