THE ROLE OF AI ENHANCING DRUG SAFETY FOCUS ON DRUG-DRUG INTERACTIONS

pharmacy

Drug safety remains a critical component of modern healthcare, and Drug-Drug Interactions (DDIs) represent a major preventable cause of adverse drug reactions. Traditional methods for identifying DDIs such as clinical trials, spontaneous reporting systems, and manual literature review are often limited by underreporting, high complexity, and delayed detection. Artificial Intelligence (AI) has emerged as a powerful tool capable of overcoming these limitations by analysing large, diverse biomedical datasets and identifying hidden patterns associated with potential interactions. Recent advancements in machine learning, deep learning, graph-based modelling, and natural language processing have significantly improved the accuracy and efficiency of DDI prediction. This review provides an overview of the role of AI in enhancing drug safety, describing the major computational techniques used for DDI prediction and the fundamental data sources that support AI-driven pharmacovigilance. Key applications and case studies highlight AI’s growing utility in early risk detection, personalized interaction assessment, and automated safety surveillance. Despite these advancements, challenges such as data quality issues, algorithmic bias, lack of transparency, and limited clinical validation continue to restrict widespread implementation. Future developments including explainable AI, federated learning, real-time decision support systems, and multi-omics integration are expected to strengthen predictive pharmacovigilance and support safer therapeutic decisions. Overall, the integration of AI into DDI prediction represents a promising step toward proactive, efficient, and patient-centred drug safety monitoring.

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