Artificial Intelligence in Pharmacy Practice: Applications, Benefits, Challenges, And Future Perspectives

Artificial intelligence (AI) is rapidly reshaping pharmacy practice, extending across drug discovery, clinical decision support, medication dispensing, pharmacovigilance, precision dosing, and patient engagement. This narrative review synthesizes current peer-reviewed literature to describe the principal applications, benefits, challenges, and future directions of AI within pharmacy practice. A structured search of PubMed, ScienceDirect, and Google Scholar identified relevant reviews, primary studies, and commentaries published primarily between 2010 and 2026. AI technologies—including machine learning, deep learning, natural language processing, and, more recently, generative AI and large language models—have been applied to accelerate target identification and molecule design, to power clinical decision support systems that flag drug–drug interactions and potentially inappropriate prescriptions, to automate dispensing through robotics, to detect adverse drug reactions from structured and unstructured data sources, to individualize dosing through pharmacogenomic and pharmacokinetic modelling, and to support medication adherence through chatbots, smart devices, and predictive analytics. Reported benefits include reductions in dispensing and prescribing errors, shorter turnaround times, cost savings, and improved patient adherence and satisfaction. However, adoption is constrained by concerns surrounding data privacy, algorithmic bias, poor interoperability, a scarcity of prospective clinical validation, unresolved questions of legal liability, and workforce readiness. Looking forward, the literature points toward explainable and auditable AI, tighter integration of multi-omic and real-world data, clearer regulatory pathways, and a redefined pharmacist role in which pharmacists supervise, validate, and contextualize AI-generated recommendations rather than being displaced by them. Realizing the full potential of AI in pharmacy will require coordinated investment in data infrastructure, prospective clinical validation, ethical governance, and pharmacy curricula that build AI literacy among current and future practitioners.