Volume-2 ,Issue-5, May-2026

Artificial Intelligence (AI) is rapidly transforming clinical pharmacy by enhancing the accuracy, efficiency, and personalization of healthcare delivery. This review presents an overview of core AI concepts, including machine learning and deep learning, along with key data sources such as electronic health records, pharmacovigilance systems, and genomic databases that enable AI-driven applications. It discusses current applications in clinical pharmacy, including clinical decision support systems, medication therapy management, adverse drug reaction detection, drug–drug interaction identification, personalized medicine, and dose optimization. The role of AI-enabled tools such as EHR-integrated systems, chatbots, predictive analytics platforms, and mobile health applications is also highlighted. Furthermore, the review examines the major benefits of AI, including improved patient safety, reduced medication errors, enhanced clinical decision-making, and increased operational efficiency. However, challenges such as data privacy and security concerns, algorithmic bias, lack of transparency, limited clinical validation, and integration barriers are critically addressed. Finally, future perspectives focusing on precision pharmacy, integration with omics technologies, real-time patient monitoring, and the development of automated pharmacy systems are discussed. Overall, AI holds significant potential to revolutionize clinical pharmacy practice by enabling data-driven, patient-centered care, although addressing existing limitations is essential for its successful and ethical implementation.

Keyword: Artificial Intelligence, Clinical Pharmacy, Machine Learning, Deep Learning, Clinical Decision Support Systems, Personalized Medicine