How AI is transforming ICSR processing — and what FDA and EMA expect from automated safety systems
AI-assisted pharmacovigilance is no longer theoretical — sponsors are deploying ML-based triage, natural language processing for narrative generation, and automated duplicate detection at scale. The regulatory question is not whether AI can accelerate ICSR processing (it demonstrably does), but how to document the oversight framework. FDA's voluntary ICSR automation guidance and EMA's Good Pharmacovigilance Practice modules both demand that the human practitioner remains accountable: AI surfaces the case, the qualified safety professional validates, signs off, and submits. The validation package for any AI-assisted PV tool must demonstrate that the system does not introduce new error modes, that outputs are reproducible, and that there is a clear audit trail linking every automated action to a qualified reviewer. Sponsors who treat their AI PV platform as a black box — rather than a validated, documented system — face significant inspection risk.