AI in Pharmacovigilance: Faster Signal Detection, Safer Drugs

Pharmacovigilance has traditionally been a labor-intensive discipline: teams manually review case reports, literature, and spontaneous adverse-event submissions to detect safety signals. As data volumes grow, that approach struggles to keep pace. Natural-language processing now allows safety teams to scan vast streams of structured and unstructured data — case narratives, medical literature, even social listening — to surface potential signals earlier. Machine-learning classifiers can triage incoming reports by seriousness and expectedness, routing the highest-priority cases to human reviewers first. The regulatory expectation is clear: AI augments, it does not replace, qualified pharmacovigilance professionals. Taragal Research builds human-in-the-loop review into every automated workflow, ensuring that signal detection is faster without sacrificing the clinical accountability that drug safety demands.