V3I1P27

Artificial Intelligence in Pharmacovigilance: A Comprehensive Review

Geeta Sachin Rajguru^1*, Miss Poonam Yadav^1*

Abstract

Pharmacovigilance (PV) is a critical discipline dedicated to ensuring the safety of medicines throughout their lifecycle. The increasing volume of safety data generated from clinical trials, post-marketing surveillance, electronic health records, literature, and social media has created significant challenges for traditional pharmacovigilance systems. Manual processing, delayed reporting, and under-detection of adverse drug reactions (ADRs) limit the effectiveness of conventional approaches. Artificial Intelligence (AI), including machine learning (ML), deep learning (DL), and natural language processing (NLP), has emerged as a powerful solution to overcome these limitations. AI-driven techniques enable automated handling of individual case safety reports (ICSRs), early detection of safety signals, predictive risk assessment, and real-time drug safety monitoring. These technologies improve efficiency, accuracy, and consistency while reducing human workload and operational costs. This review presents a detailed overview of the role of artificial intelligence in pharmacovigilance, covering methodologies, applications, benefits, challenges, and future perspectives. The integration of AI into pharmacovigilance systems holds significant promise for enhancing patient safety and supporting regulatory decision-making.

Keywords:

Pharmacovigilance, Artificial Intelligence, Machine Learning, Adverse Drug Reactions, Drug Safety