Artificial intelligence in clinical pharmacy—A systematic review of current scenario and future perspectives

Objective Medication prescription errors represent a significant and persistent challenge within healthcare systems globally, constituting a primary focus for clinical pharmacy practice. Additional complexities involve the optimization of drug dosing and the implementation of personalized medicine. This review aims to synthesize the current advancements in artificial intelligence (AI) applications within clinical pharmacy and to discuss future directions for the field. Methods To present this narration, 30 articles were reviewed in total. The literature search was done using electronic databases, for example, PubMed, Medline, and Google Scholar, with the help of some keywords. Only articles published in peer-reviewed journals were included. Results A total of 30 articles that demonstrated the utility of AI-based applications in clinical pharmacy were included for further analysis. Across all included studies, AI was utilized primarily for the detection of adverse drug events, clinical decision support, verification of prescription accuracy, and pharmacometrics. Secondary applications included providing recommendations to pharmacists for medication therapy management and, importantly, predicting the therapeutic response to a given treatment in conjunction with its cost-effectiveness. Conclusion Artificial intelligence-based algorithms have been identified as applicable tools for the early detection of adverse drug events and prescription errors, the prediction of individual drug response, and the design of patient-specific treatment plans. Prior to broad clinical implementation, future multicenter, prospective studies employing standardized clinical endpoints, external validation, and cost-effectiveness analyses are required.