Adverse drug events (ADEs), particularly those associated with drug-drug interactions (DDIs), represent a major and preventable burden on global healthcare systems. Traditional DDI detection methods, which rely heavily on static databases and human monitoring, are increasingly insufficient given the complexity of modern pharmacotherapy. Artificial intelligence (AI), especially machine learning (ML) and natural language processing (NLP), has emerged as a transformative solution for predicting, detecting, and managing DDIs. AI systems can integrate data from electronic health records (EHRs), biomedical literature, pharmacogenomic profiles, and clinical notes to provide more accurate and patient-specific risk assessments. This article examines the role of AI in enhancing patient safety, strengthening clinical decision support systems (CDSS), enabling personalized medicine, and improving healthcare efficiency. The study also discusses implementation challenges related to data quality, interpretability, ethics, and clinical integration. Overall, AI-driven DDI prevention systems have significant potential to reduce adverse drug events and improve healthcare quality and sustainability.
Salehi et al. (Wed,) studied this question.
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