Artificial Intelligence in Pharmacovigellance: Emerging Drug Safety Monitoring

Pharmacovigilance is an important aspect of healthcare that is dedicated to the identification, evaluation, interpretation, and prevention of adverse drug reactions (ADRs) to guarantee patient safety. Conventional pharmacovigilance systems use spontaneous reporting, clinical trials and manual signal-data analysis, but these approaches are susceptible to underreporting, slow signal recognition, and limited data analysis. Artificial Intelligence (AI) is one of the latest technologies that have changed the landscape of drug safety monitoring in the last few years by allowing quicker and more precise and data-driven decisions. This review discusses AI in pharmacovigilance focusing on the new drug safety monitoring systems. Machine learning, deep learning, and natural language processing (NLP) AI tools are getting more commonly used to process large and multifaceted healthcare data, such as electronic health records (EHRs), clinical databases, scientific literature, and social media platforms. These technologies enhance detection of adverse drug reactions, signal detection, and the risk assessment, and decrease the human error and workload. Pharmacovigilance systems, which are based on AI, also promote real-time monitoring and predictive analytics, which can identify possible safety risks before they become widespread. Moreover, AI can be incorporated with big data analytics and digital health platforms to increase the effectiveness and scale of international drug safety surveillance. Although these benefits exist, issues like data privacy, absence of standardized data, ethical concerns, and barriers to implementation need to be managed to achieve successful adoption. Altogether, AI can transform pharmacovigilance by transforming drug safety surveillance into a proactive and predictive method, which will ultimately enhance patient outcomes and enhance healthcare systems.