(untitled) (International Journal of Research in Pharmaceutical Sciences and Technology, 2026)
Conventional pharmacotherapy often follows a one-size-fits-all approach, despite significant differences in patients’ genetics, age, comorbidities, and drug responses. Artificial Intelligence (AI) offers an emerging approach to transform personalized pharmacotherapy by integrating clinical, genetic, and patient-specific information. This review explores the potential of AI in optimizing drug selection, dosage, therapeutic response, and medication safety. AI-driven clinical decision-support systems can analyze complex patient data to predict treatment responses, identify potential adverse drug reactions, detect drug–drug interactions, and support individualized dose optimization. Integration with pharmacogenomics may further enable prediction of patient-specific drug responses and improve therapeutic outcomes. Pharmacists can contribute significantly by validating AI-generated recommendations, monitoring medication safety, and ensuring patient-centred implementation. However, challenges including data privacy, algorithmic bias, transparency, ethical concerns, and clinical validation require careful consideration. AI-driven personalized pharmacotherapy has the potential to shift healthcare from standardized prescribing toward safer, more precise, and individualized medication management.