We focus here on the application of AI to the processing of data from multiple sources to identify adverse events (AEs) meeting regulatory reporting requirements, the preparation of these AEs as ICSRs, and their further reporting and evaluation. Although FDA is exploring the use of AI in many of these areas, research in these areas is not yet mature enough to consider widespread implementation from a regulatory perspective. FDA’s definition of PV is broad and includes the use of a wide range of scientific inquiry, such as Individual Case Safety Reports (ICSRs), pharmacoepidemiologic studies, registries, clinical pharmacology studies, and other approaches. The US FDA defines PV as “all scientific and data gathering activities relating to the detection, assessment, and understanding of adverse events”. There is much excitement about the application of ‘artificial intelligence’ (AI) approaches to drug 1 development and lifecycle drug management, including pharmacovigilance (PV). Introduction-The Need for AI in Pharmacovigilance 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. 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. 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 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. 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). There is great interest in the application of ‘artificial intelligence’ (AI) to pharmacovigilance (PV).
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