APPLICATION OF ARTIFICIAL INTELLIGENCE IN PHARMACOVIGILANCE FOR AUTOMATED ADVERSE DRUG REACTION DETECTION AND REGULATORY COMPLIANCE

Abstract

Pharmacovigilance is essential for continuous monitoring of medicine safety and prevention of adverse drug reactions. Traditional pharmacovigilance approaches depend mainly on spontaneous reporting and manual evaluation, resulting in underreporting, delayed signal identification and difficulties in managing large healthcare datasets. Artificial intelligence (AI) has emerged as a powerful technology for improving drug safety surveillance through automated data processing, pattern recognition and predictive analysis. Machine learning, deep learning and natural language processing enable efficient extraction of safety information from structured and unstructured sources including electronic health records, clinical trials, scientific literature, social media and regulatory databases. AI-based pharmaco vigilance systems support automated adverse drug reaction detection, individual case safety report processing, signal prioritization and regulatory compliance. Major databases such as FAERS, Vigi Base and Eudra Vigilance provide important resources for AI model development and validation. However, challenges related to data quality, transparency, privacy, algorithm validation and regulatory acceptance must be addressed for reliable implementation. This review discusses AI applications in pharmacovigilance, data sources, regulatory considerations, limitations and future opportunities for developing proactive and intelligent drug safety systems.