ARTIFICIAL INTELLIGENCE IN DRUG SAFETY AND PHARMACOVIGILANCE

Pharmacovigilance focuses on monitoring the safety of medicines by identifying, evaluating, and preventing adverse drug reactions after a drug is introduced into clinical use. Adverse drug reactions are unintended and harmful effects that may occur following the use of medicines and can significantly affect patient safety and treatment outcomes. The conventional pharmacovigilance process includes several stages such as the collection of safety data, medical review of case reports, coding of clinical information, causality assessment, and submission of reports to regulatory authorities. While these activities are essential for drug safety, they often require substantial time, skilled manpower, and technical resources, which can limit efficiency in large-scale safety monitoring. Recent developments in artificial intelligence have provided innovative tools to address these limitations in pharmacovigilance. Machine learning and natural language processing techniques enable automated analysis of large and complex safety datasets obtained from sources such as electronic health records, spontaneous reporting systems, and patient-reported data. These technologies support faster identification of adverse drug reactions, early detection of safety signals, and improved prediction of drug-drug interactions. By reducing manual workload, Al allows pharmacovigilance professionals to focus more on clinical evaluation and decision-making, thereby enhancing the overall quality of safety assessments. 0In addition to improving operational efficiency, artificial intelligence supports a shift towards more proactive and real-time drug safety surveillance. However, the integration of Al into pharmacovigilance also presents challenges, including concerns related to data privacy, algorithm bias, transparency, and regulatory acceptance. Addressing these issues through proper validation, ethical use, and regulatory alignment is crucial for the successful implementation of Al-based systems. This article discusses the role of artificial intelligence in strengthening pharmacovigilance practices and highlights its potential to improve accuracy, responsiveness, and public health protection.