Artificial Intelligence–Enabled Decision Support in Pharmacy Practice: Development and Evaluation of a Predictive Framework

Background: Artificial Intelligence (AI) has demonstrated growing potential in improving pharmacy practice by supporting clinical decision-making, medication safety, and operational efficiency. However, empirical research evaluating AI-based decision-support frameworks in pharmacy settings remains limited. Objective: To develop and evaluate an AI-enabled predictive decision-support framework for medication safety and therapy optimization in pharmacy practice. Methods: A structured AI framework was developed using supervised machine learning algorithms trained on anonymized secondary clinical datasets, including medication profiles, laboratory values, and adverse drug reaction (ADR) reports. Model performance was evaluated using accuracy, precision, recall, and F1-score. Comparative analysis was conducted against traditional rule-based systems. Results: The proposed AI framework demonstrated superior predictive performance compared to conventional systems, achieving an accuracy of 92.4% in detecting potential medication-related risks. The model effectively identified drug–drug interactions, dose-related issues, and high-risk patient profiles, reducing false-positive alerts by 28%. Conclusion: AI-enabled decision-support systems can significantly enhance pharmacy practice by improving medication safety and clinical efficiency. Integration of such systems with pharmacist oversight offers a promising approach for advancing patient-centered pharmaceutical care.