Artificial Intelligence in Pharmacovigilance: Current Applications, Opportunities, Challenges, and Future Perspectives

Pharmacovigilance (PV) is a critical component of healthcare systems that aims to ensure the safe and rational use of medicines through the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs). Large volumes of safety data that contradict traditional pharmacovigilance methods have been produced by the quick growth of healthcare databases, electronic health records, spontaneous reporting systems, biomedical literature, and social media platforms. A promising technology for improving pharmacovigilance efforts is artificial intelligence (AI), which includes machine learning (ML), deep learning (DL), natural language processing (NLP), and generative AI. AI-driven methods increase the effectiveness and precision of medication safety monitoring by enabling automated case processing, signal detection, literature screening, duplication identification, and predictive safety evaluations. Widespread adoption is still hampered by issues like poor data quality, algorithmic bias, explainability issues, privacy concerns, and regulatory difficulties. The principles and uses of AI in pharmacovigilance are covered in this study, along with key potential and difficulties, existing regulatory perspectives, and future directions for AI-enabled drug safety monitoring.