This white paper examines how artificial intelligence can improve pharmacovigilance signal management by helping teams identify, assess, and prioritize emerging drug safety risks. Traditional processes rely heavily on manual review and established statistical methods, which can be resource intensive and generate many false positives as data volumes and sources expand. AI, natural language processing, predictive analytics, and agentic systems can extract relevant evidence, analyze real-world data, detect patterns, contextualize findings, and recommend next steps while preserving traceability and human oversight. These capabilities may support faster, more accurate, and proactive safety decisions, reduce operational burden, and help organizations move beyond retrospective monitoring toward c

Join for free to read