AI-BASED ADVERSE DRUG REACTION PREDICTION AND THERAPEUTIC SAFETY: A REVIEW

Abstract

Adverse drug reactions (ADRs) remain a major challenge in healthcare, contributing to patient morbidity, mortality, and increased healthcare costs. Effective therapy safety requires timely detection, assessment, and prevention of drug-related adverse events. Traditional pharmacovigilance systems rely on spontaneous reporting and manual data analysis, which are often limited by underreporting and delayed signal detection. Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), has emerged as a transformative tool in healthcare and drug safety monitoring. AI-based predictive models utilize electronic health records, clinical databases, social media, and biomedical literature to identify potential ADRs and drug-drug interactions. Natural Language Processing (NLP) and data mining techniques further enhance ADR detection and risk prediction. AI also supports personalized medicine and clinical decision support systems, improving therapeutic outcomes and patient safety. Despite challenges related to data privacy, ethics, and model reliability, recent advancements highlight the growing potential of AI-driven pharmacovigilance. This review explores the role of AI in ADR prediction, therapy safety, current developments, and future prospects in drug safety management.