| アブストラクト | There is great interest in the application of 'artificial intelligence' (AI) to pharmacovigilance (PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application of AI to the processing and evaluation of Individual Case Safety Reports (ICSRs) submitted to the FDA Adverse Event Reporting System (FAERS). We describe a general framework for considering the readiness of AI for PV, followed by some examples of the application of AI to ICSR processing and evaluation in industry and FDA. We conclude that AI can usefully be applied to some aspects of ICSR processing and evaluation, but the performance of current AI algorithms requires a 'human-in-the-loop' to ensure good quality. We identify outstanding scientific and policy issues to be addressed before the full potential of AI can be exploited for ICSR processing and evaluation, including approaches to quality assurance of 'human-in-the-loop' AI systems, large-scale, publicly available training datasets, a well-defined and computable 'cognitive framework', a formal sociotechnical framework for applying AI to PV, and development of best practices for applying AI to PV. Practical experience with stepwise implementation of AI for ICSR processing and evaluation will likely provide important lessons that will inform the necessary policy and regulatory framework to facilitate widespread adoption and provide a foundation for further development of AI approaches to other aspects of PV. |
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| ジャーナル名 | Drug safety |
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| Pubmed追加日 | 2022/5/18 |
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| 投稿者 | Ball, Robert; Dal Pan, Gerald |
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| 組織名 | US Food and Drug Administration, Center for Drug Evaluation and Research, Office;of Surveillance and Epidemiology, Silver Spring, MD, USA.;Robert.Ball@fda.hhs.gov. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/35579808/ |
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