Artificial Intelligence (AI)-driven pharmacovigilance is transforming drug safety by enhancing the detection, assessment, understanding, and prevention of adverse drug reactions (ADRs). Traditional pharmacovigilance systems often rely on manual reporting processes, which are time-consuming, resource-intensive, and limited by underreporting and delayed signal detection. This paper explores the integration of advanced AI technologies—including machine learning (ML), natural language processing (NLP), deep learning, and big data analytics—into pharmacovigilance frameworks to improve efficiency, accuracy, and real-time monitoring of drug safety. AI-powered systems can analyze vast volumes of structured and unstructured healthcare data from sources such as electronic health records (EHRs), social media, clinical trial reports, and spontaneous reporting systems to identify safety signals more rapidly than conventional methods. The study highlights key innovations, including automated case processing, predictive risk modeling, duplicate detection, and signal prioritization, while also examining regulatory considerations and implementation challenges such as data privacy, algorithm transparency, and validation requirements.
Rathore Vivek Singh*, Sawner Yashraj, Dawar Sachin, Jaiswal Manoj (Mon,) studied this question.