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Pharmacovigilance has evolved from a system based mainly on spontaneous adverse drug reaction reporting into a multidimensional discipline that combines global case-report databases, active surveillance, pharmacoepidemiology, digital health records, patient-generated information and advanced analytics. This expansion is necessary because pre-authorisation trials cannot fully identify rare, delayed, interaction-related or population-specific harms that emerge during routine use. This narrative review examines the architecture of international pharmacovigilance, major surveillance platforms, signal-detection approaches, clinical and regulatory risk assessment, and current developments in artificial intelligence and real-world evidence. The World Health Organization Programme for International Drug Monitoring and VigiBase provide a framework for cross-national learning, while regional and national systems such as EudraVigilance, the United States Food and Drug Administration Adverse Event Reporting System and India's Pharmacovigilance Programme of India support local reporting and regulatory action. Disproportionality methods, including the reporting odds ratio, proportional reporting ratio, information component and empirical Bayes approaches, are useful screening tools but do not establish causality or incidence. Active surveillance and well-designed non-interventional studies can strengthen signal evaluation by supplying denominators, comparators and longitudinal outcomes. Artificial intelligence can assist case processing, coding, duplicate detection and pattern recognition; however, biased data, limited external validation, weak explainability and automation errors require human oversight. Recent ICH E2D (R1) and M14 guidance further emphasise fit-for-purpose safety data and harmonised study methods. The review concludes that effective pharmacovigilance depends not on data volume alone but on reliable reporting, transparent analysis, timely risk communication, equitable global participation and practical measures that improve patient outcomes.
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Chawla et al. (2026) studied this question.
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