Artificial Intelligence in Pharmacovigilance: Emerging Application for Enhanced Patient Safety

Abstract

The goal of pharmacovigilance is to monitor, comprehend, and prevent adverse drug reactions (ADRs), unpleasant side effects, and other issues. From primarily manual adverse drug reaction (ADR) monitoring, pharmacovigilance has evolved into a technology-supported field that can handle sizable and intricate safety datasets. In order to find potential links between pharmaceuticals and adverse occurrences, data-mining and machine-learning approaches have been increasingly used to databases of spontaneous reports. Natural language processing (NLP) and more advanced machine-learning algorithms led to a significant expansion of artificial intelligence applications during the 2010s. To find trends in pharmacovigilance data and assist in the identification of possible drug-safety alerts, machine learning employs statistical and computational methods. A new method for forecasting adverse drug responses and other medication-related safety outcomes is machine-learning-based predictive modeling. AI-assisted risk assessment uses computer methods to identify and forecast possible safety issues related to pharmaceuticals. AI can find trends linked to adverse medication occurrences by analyzing data from pharmacovigilance databases, electronic health records, and other real-world data sources.