AI in Pharmacovigilance: Predictive Analytics for Drug Safety and Post-Marketing Surveillance

Abstract

Artificial intelligence (AI) is revolutionizing pharmacovigilance (PV) by enabling faster, more accurate detection and management of adverse drug reactions (ADRs). Advanced AI systems, including machine learning algorithms, large language models, and deep-signal detection tools, are being integrated into regulatory frameworks and industry workflows to enhance drug safety monitoring. Platforms such as WHO-UMC VigiLyze and the FDA Sentinel Initiative demonstrate the practical application of AI in improving signal detection, risk prediction, and proactive safety interventions. Despite its promise, AI adoption in PV presents challenges, including algorithmic bias, explainability, data privacy concerns, and integration with legacy systems. Future trends point toward autonomous AI-driven PV, federated learning for global safety analysis, hybrid human-AI decision-making, and predictive digital twins for individualized ADR risk assessment. Collectively, these innovations are poised to transform pharmacovigilance into a more intelligent, efficient, and proactive discipline.