AI-Driven Pharmacovigilance: New Strategies for Detecting and Monitoring Adverse Drug Reactions

Abstract

Pharmacovigilance plays an important role in identifying, evaluating, and monitoring adverse drug reactions throughout the life cycle of medicines. Traditional pharmacovigilance systems depend mainly on spontaneous reports, clinical records, regulatory databases, and other safety information. The growing volume and complexity of these data have created a need for faster and more systematic methods of safety monitoring. Artificial intelligence (AI) is being increasingly studied for this purpose. Machine learning, natural language processing, deep learning, and other AI techniques contribute in the detection of safety signals from structured and unstructured data sources. These methods assist in analysing adverse event reports, electronic health records, clinical data, social media content, and patient-generated information. AI can also improve signal prioritization, adverse drug reaction classification, causality assessment, and prediction of potential drug-related risks. This review discusses the emerging applications of AI in pharmacovigilance, with emphasis on adverse drug reaction detection and post-marketing safety monitoring. Recent advances in transformer-based language models and real-world data integration have expanded the potential applications of AI across pharmacovigilance workflows. Even after these advancements, successful implementation depends on high-quality data, external validation, explainable AI, and continued human oversight. Future research should focus on robust clinical validation and responsible regulatory integration to ensure safe and reliable AI-assisted pharmacovigilance.