Transforming Global Pharmacovigilance through AI and Real-World Evidence

Abstract

Objective: The primary goal of this study is to evaluate the transformative potential of integrating Artificial Intelligence (AI) and Real-World Data (RWD) within global pharmacovigilance frameworks to automate adverse drug reaction detection, enhance regulatory decision-making efficiency, and establish a data-driven standard for precision medicine. To demonstrate how these innovations can align the Pharmacovigilance Programme of India (PvPI) with international benchmarks like the FDA's Sentinel System to set a new global standard for drug safety surveillance. Key Findings: Incorporating Natural Language Processing (NLP) and Machine Learning into high-speed data surveillance enables proactive signal detection using RWD from EHRs and the Ayushman Bharat Digital Mission. Combining AI capabilities with trained human expertise accelerates drug safety workflows, optimizes clinical trial efficiency, and transforms proactive public health monitoring.