AI-Driven Pharmacovigilance: Advances in Adverse Drug Reaction Detection and Signal Management - A Review

Abstract

Pharmacovigilance plays a critical role in drug safety through its process of monitoring, detecting, and preventing adverse drug reactions (ADRs). Traditional pharmacovigilance methods depend on manual data processing together with spontaneous reporting systems which require extensive time for the execution of their work but lack operational efficiency. Artificial intelligence (AI) has developed into a revolutionary healthcare solution during the past several years because it provides organizations with powerful data processing and pattern discovery abilities. The study demonstrates how AI technology functions in pharmacovigilance through its two main functions of ADR detection and signal management. AI-based techniques use machine learning and natural language processing to process extensive data from multiple sources which include electronic health records and clinical databases and social media platforms. The current approaches enable organizations to identify drug safety signals at an early stage while achieving higher precision in their assessment process and conducting ongoing safety surveillance. AI helps organizations to verify their signals which enables them to establish signal importance and make decisions about regulatory matters. The use of AI in pharmacovigilance shows benefits but organizations need to deal with three major obstacles which include data quality issues and privacy risks and regulatory restrictions. The research demonstrates that AI-powered pharmacovigilance systems will enhance drug safety monitoring together with healthcare results while establishing more efficient and proactive pharmacovigilance systems for upcoming development.