AI-Driven Pharmacovigilance: Leveraging Big Data for Improved Adverse Event Detection

To protect public health, pharmacovigilance—which monitors the safety of medications throughout their life cycle—is crucial. Underreporting, delays, and fragmented data frequently restrict the efficacy of standard pharmacovigilance systems. Manual data review and spontaneous adverse drug reaction (ADR) reporting are the fundamental pillars of these systems. The rise of digital health ecosystems and the rapid accumulation of diversified, large-scale healthcare data, or Big Data, have led to an increasing interest in integrating artificial intelligence (AI) to improve pharmacovigilance outcomes. This analysis centers on the ways in which artificial intelligence (AI) technologies—like machine learning, deep learning, and natural language processing (NLP)—are altering traditional techniques for identifying ADRs. Real-time monitoring, improved pattern identification, and early signal detection are all made feasible by AI algorithms’ capacity to handle organized and unstructured data from a range of sources. Regulatory agencies and the pharmaceutical industry have demonstrated through case studies and ongoing applications how AI may be used to automate safety procedures and improve post-marketing surveillance. But in order to ensure safe and effective adoption, problems like model transparency, data heterogeneity, regulatory alignment, and ethical considerations must be fixed. Ultimately, the integration of AI and Big Data analytics into pharmacovigilance signifies a paradigm shift towards a more proactive, accurate, and scalable drug safety monitoring system.