Artificial Intelligence in Pharmacy: Applications, Opportunities and Challenges

Artificial intelligence (AI) is reshaping every stage of the medicine life cycle, from the identification of drug targets to the counselling of patients at the pharmacy counter. This review examines how AI technologies, chiefly machine learning, deep learning, natural language processing and generative models, are applied across pharmaceutical science and pharmacy practice, and weighs the opportunities they offer against the challenges that limit their safe adoption. In drug discovery, AI supports target identification, protein structure prediction, virtual screening, de novo molecular design, retrosynthesis, drug repurposing and clinical trial optimisation. In pharmaceutics and manufacturing, it is used to predict formulation behaviour, guide three-dimensional printing of dosage forms and enable real-time process control. In clinical, hospital and community pharmacy, AI-enabled tools assist prescription verification, prevention of adverse drug events, dose individualisation, adherence support, inventory management and patient communication. AI also strengthens pharmacovigilance, regulatory science and pharmacy education. The principal opportunities are shorter development timelines, lower attrition, personalised therapy, safer medication use and more efficient operations. The principal challenges are poor data quality and bias, limited transparency of complex models, privacy, unclear accountability, evolving regulation, validation gaps, fabricated output from language models and the need to prepare the pharmacy workforce. We conclude that AI is best viewed as a decision-support partner that extends, and does not replace, the professional judgement of pharmacists and pharmaceutical scientists, and we outline priorities for responsible implementation.