Systematic review of AI-based models in pharmacoepidemiology for adverse drug event prediction and detection.

Introduction: Artificial intelligence (AI) has increasingly been applied in pharmacoepidemiology, yet the methodological landscape of adverse drug event (ADE) prediction remains heterogeneous and insufficiently mapped. Methods: This systematic review aimed to characterize contemporary AI-based approaches used to detect or predict ADEs in real-world clinical data. Following PRISMA 2020 guidelines and a registered protocol (PROSPERO: CRD420251159394), 281 records were screened and 15 studies met the inclusion criteria. Results: All included studies relied primarily on structured electronic health records (EHRs) or administrative claims, while only a minority incorporated natural language processing (NLP) components, and none used spontaneous reporting systems as the primary analytic datasets. Tree-based ensemble models (e.g., Random Forests, XGBoost) and regularized regression were the most commonly adopted algorithms, whereas deep learning architectures appeared less frequently and typically required temporal or representation-based inputs. Through studies, external or temporal validation was rarely performed and explainability methods were inconsistently applied, limiting generalizability. No standardized benchmarks were identified, and reporting practices varied substantially. Discussion: Future work should emphasize rigorous validation, transparent model reporting, and the careful integration of NLP and explainability frameworks to support clinically reliable and scalable pharmacoepidemiological applications.