The Future of Drug Safety: AI-Driven Detection and Prediction of Adverse Drug Reactions

Abstract

Pharmacovigilance (PV) is pivotal in ensuring drug safety and minimizing adverse drug reactions (ADRs). With the growing complexity of drug development, rising ADR-related morbidity, and the proliferation of real-world and digital health data, traditional PV systems face significant challenges, including underreporting, signal latency, and heterogeneous data integration. Recent advances in artificial intelligence (AI) offer transformative potential to enhance ADR detection, signal management, and personalized risk prediction. This review explores the evolution of pharmacovigilance, from early frameworks to modern AI-enabled systems, examining core concepts, regulatory landscapes, and the integration of machine learning, deep learning, natural language processing, and predictive modeling into PV practice. The study also evaluates digital data sources such as electronic health records, social media, and real-world evidence, highlighting AI-driven tools for real-time surveillance and automated signal detection. Ethical, legal, and regulatory considerations, along with challenges in implementation, transparency, and data quality, are discussed. Finally, emerging technologies including multi-omics integration, blockchain, digital twins, and predictive pharmacovigilance in drug development are considered, emphasizing the shift from reactive to proactive drug safety monitoring. This comprehensive analysis underscores the opportunities and future directions for AI in improving patient safety and advancing global pharmacovigilance systems.