Journal of Pharmacovigilance and Quality Assurance ISSN: 3108-2343 (Online)

Author: Neelam Rani

Abstract: Pharmacovigilance plays a critical role in ensuring the safety of pharmaceutical products by identifying, evaluating, and preventing adverse drug reactions (ADRs). Traditional pharmacovigilance methods, while effective, often face challenges in processing vast amounts of data from diverse sources, which limits the ability to detect potential risks early. The rise of Artificial Intelligence (AI) and Machine Learning (ML) offers transformative opportunities to enhance drug safety monitoring. This paper explores how AI and ML techniques, such as predictive analytics, natural language processing, and deep learning, can be leveraged to improve pharmacovigilance by enabling faster detection of ADRs, predicting safety signals, and improving decision-making processes in drug safety. We also discuss the integration of AI and ML in real-world data and pharmacovigilance systems, highlighting challenges, regulatory considerations, and future prospects for these technologies in drug safety surveillance.

Keywords: Pharmacovigilance, Artificial Intelligence, Machine Learning, Drug Safety, Adverse Drug Reactions, Predictive Analysis, Early Detection, Surveillance, Signal Detection, Natural Language Processing, Real-World Data, Drug Safety Monitoring