Cutting Through the Literature Queue: What AI Screening Enables for Pharmacovigilance Teams

 The first task of a Monday morning for many pharmacovigilance literature teams looks the same week after week: opening a review queue that has accumulated new articles across the weekend. Before any medical review begins, a reviewer must determine which articles contain safety-relevant information, which are duplicates of records already processed, which are pharmacokinetic studies or in-vitro research that mention a drug only in passing, and which contain a genuine adverse event report that may need to become an Individual Case Safety Report (ICSR).



That determination, performed manually, is where a significant portion of the pharmacovigilance team’s effort disappears each week. It is also the portion where AI can deliver the greatest operational value in literature review automation.

The efficiency case for AI-assisted literature screening in pharmacovigilance is well supported by research. A synthesis published in Frontiers in Pharmacology in January 2025, drawing on multiple structured literature review automation studies, found that AI-assisted screening tools can reduce the volume of articles requiring human review to as low as 23% of the total retrieved, with time savings per review cycle ranging from 7 to 86 hours. While this isn’t exactly a theoretical reduction, it changes what pharmacovigilance professionals are doing each week and determines whether recovered hours are allocated to high-value medical analysis or returned to the same manual queue.

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