Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review.

Background: Adverse drug events (ADEs) remain a critical safety issue in pharmaceutical research and development (Pharma R&D), necessitating robust methods for early detection and surveillance. Language models (LMs) are increasingly used in ADE analysis, addressing safety challenges during drug development and postmarket surveillance. Language modeling approaches, ranging from static embeddings to large language models (LLMs), 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. Objective: This scoping review aims to map the application of LMs for the analysis of ADEs across the Pharma R&D lifecycle. Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, we searched PubMed, Web of Science, and Google Scholar for relevant papers published between January 2015 and October 2025. Results: This review identified 49 relevant papers. Overall, LM applications in Pharma R&D safety analysis are concentrated in 2 distinct phases: ADE prediction during the premarket phase (n=16) and ADE detection in postmarket surveillance (n=33). Conclusions: While some models demonstrate high predictive performance, persistent challenges, including data heterogeneity and limited external validation, hinder widespread adoption. Despite these barriers, discriminative and generative LMs have the potential to transform drug safety across the pre- and postapproval phases, especially when integrated with real-world pharmacovigilance frameworks.