Integrating AI in Pharmacovigilance: A New Era of Drug Safety and Risk Management

Abstract

Traditional pharmacovigilance methods are becoming less and less effective at controlling medication safety as a result of the growing complexity of healthcare systems and the proliferation of real-world data. This review discusses how pharmacovigilance is changing from a reactive process to a proactive and predictive strategy for tracking adverse drug reactions (ADRs) with the help of Artificial Intelligence (AI). By using technologies like Machine Learning (ML), Natural Language Processing (NLP), and deep learning, artificial intelligence (AI) can quickly analyze various data sources, including wearable technology, social media, and electronic health records, to find safety issues earlier and more accurately. This innovation promises improved risk-benefit analysis, faster decision-making, and better patient outcomes. The adoption of AI-driven pharmacovigilance systems has advanced significantly in nations like the US, EU, China, and India; nevertheless, issues with data quality, transparency, regulatory alignment, and workflow integration still exist. The review also covers the useful applications of AI, like risk-benefit analysis, automating case processing, and creating predictive models for ADR detection. Despite encouraging developments, we must resolve ethical issues, understandability limitations, infrastructure deficiencies, and bias in AI models to ensure safe and fair adoption. To fully utilize AI in pharmacovigilance, a well-rounded, cooperative strategy involving developers, data scientists, regulators, and physicians is essential. In the end, AI is a strong complement to human-led drug safety initiatives rather than a substitute.