AI-DRIVEN INNOVATIONS IN PHARMACOVIGILANCE: TRANSFORMING DRUG SAFETY SURVEILLANCE FOR THE FUTURE

The practice of Pharmacovigilance focuses on identifying, evaluating and preventing drug-related adverse events, also referred to as ADRs, which ensure the safety of pharmaceuticals. Regarding these changes in healthcare systems, it is becoming very important to implement new technologies, such as artificial intelligence, in pharmacovigilance operations that promise to enhance drug safety monitoring. Such AI devices as Natural Language Processing (NLP), Machine Learning (ML), Deep Learning and Big Data Analysis are changing the landscape of Primary Adverse Drug Reactions (ADRs): detection, analysis, and reporting. These technologies allow for faster processing, attention and early response to signals, and other problems that have always been creating barriers such as late signal detection, and low levels of reporting. This paper highlights the impact of AI in the process of modernizing the steps of pharmacovigilance, including data analysis and making predictions about involved cases. Also, we elaborate on the ways AI strengthens regulatory reporting, stakeholder engagement, and risk minimization through superior signal detection. However, employing AI for pharmacovigilance also creates some ethical and legal issues such as the privacy of data, biases of the algorithms, and compliance with regulations. Addressing these aspects is essential to ensure AI-driven pharmacovigilance systems are reliable, fair, and aligned with ethical standards. This articlefocuses on how AI could make pharmacovigilance more proactive, Accurate, data-driven, and effective in safeguarding public health.