JMIR Publications

ABSTRACT

The early detection of adverse drug events (ADEs) became a critical issue in clinical research after the thalidomide disaster in 1961, which resulted in the birth of thousands of babies with severe birth defects. Artificial intelligence (AI) is increasingly used in ADE prediction and detection, addressing safety challenges during drug development and post-market surveillance. AI methodologies, ranging from traditional machine learning to graph neural networks and transformer-based architectures, capitalize on diverse data sources, such as clinical trial datasets, electronic health records, and social media posts, to predict ADEs, analyze real-world evidence, and improve drug screening and pharmacovigilance systems. This review identified 81 relevant articles published between January 2015 and December 2022 following the PRISMA-ScR guidelines. Overall, AI models are applied to two drug development phases: ADE prediction during drug development (n=37) and ADE detection in post-market (n=44). While some models demonstrate high predictive performance, persistent challenges, including data heterogeneity and limited external validation, hinder widespread adoption. Despite these barriers, AI-based ADE detection can potentially transform drug safety across the pre- and post-approval phases, especially when integrated with real-world pharmacovigilance frameworks.