Contemporary Research Analysis Journal

: 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.

Artificial Intelligence, Drug-Drug Interactions, Adverse Drug Events, Clinical Decision Support Systems, Patient Safety, Healthcare Quality, Machine Learning, Pharmacovigilance.

1. Bates, D. W., Singh, H., & Mort, E. (2022). Two decades since “To Err Is Human”: Progress, but still a long way to go. The New England Journal of Medicine, 387(19), 1733–1737.
2. Dalianis, H., Henriksson, A., & Skeppstedt, M. (2020). Using machine learning and natural language processing to predict drug-drug interactions. Studies in Health Technology and Informatics, 270, 432–436.
3. Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2022). Explainable AI methods – A brief overview. In xxAI – Beyond Explainable AI (pp. 13–38). Springer.
4. Huser, V., & Cimino, J. J. (2019). Linking pharmacogenomic knowledge with patient data to drive personalized medicine. Journal of the American Medical Informatics Association, 26(11), 1215–1220.
5. Liao Q, Zhang Y, Chu Y, et al. Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review. npj Biomed Innov. 2, 1 (2025).
6. Nanji, K. C., Slight, S. P., Seger, D. L., et al. (2018). Overrides of medication-related clinical decision support alerts in outpatients. Journal of the American Medical Informatics Association, 25(5), 476–481.
7. Ogbuagu, O. O., Mbata, A. O., Balogun, O. D., et al. (2023). Artificial intelligence in clinical pharmacy: Enhancing drug safety, adherence, and patient-centered care. International Journal of Multidisciplinary Research and Growth Evaluation, 4(1), 814–822.
8. Percha, B., Zhang, Y., Noprianty, S., & Altman, R. B. (2019). Discovering drug-drug and drug-disease interactions using large-scale literature mining. Pacific Symposium on Biocomputing, 24, 389–400.
9. Reis, Z. S. N., Pereira, G. M. V., Dias, C. D. S., et al. (2025). Artificial intelligence-based tools for patient support to enhance medication adherence: A focused review. Frontiers in Digital Health, 7, 1523070.
10. Salehi, F. (2025). Boardroom AI: the governance of AI-assisted corporate decision-making. Global J Econ Finance Res, 4, 232-235.
11. Salehi, F. (2024). The transformative role of artificial intelligence in the healthcare industry: A comprehensive analysis. Asian Journal of Research in Medicine and Medical Science, 6(1), 62–70.
12. Salehi, F. (2022). Role of artificial intelligence on agile planning and organizational performance in the ICT industry. Asian Journal of Economics, Finance and Management, 4(1), 581–586.
13. Sharmila, K. S., & Chandra, K. R. (2024). Predicting adverse interactions: A comprehensive review of AI-driven drug-drug interaction models for enhanced patient safety. Proceedings of the International Conference on IoT Based Control Networks and Intelligent Systems, 1098–1102.
14. Sutton, R. T., Pincock, D., Baumgart, D. C., et al. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17.
15. Zheng, Y., Cui, Y., & Zhang, Y. (2021). Clinical decision support for drug-drug interaction prevention: A systematic review. Journal of Healthcare Engineering, 2021, 1–11.
16. Zitnik, M., Agrawal, M., & Leskovec, J. (2018). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 34(13), i457–i466.