Volume 3 ; Issue 2 ; in Month : July-Dec (2026) Article No : 122
Kumar G, Archana, Kumar R.

Abstract

Artificial intelligence (AI) has emerged as a major enabling technology in pharmaceutical sciences, healthcare delivery and biomedical research. The increasing availability of large chemical, biological, genomic, clinical and real-world datasets, together with advances in machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, robotics and generative AI, has created new opportunities throughout the pharmaceutical lifecycle. This review critically examines the evolving role of AI in pharmacy education, pharmaceutical research and drug discovery, clinical pharmacy, patient care, pharmacovigilance and personalized medicine. In pharmacy education, AI-supported adaptive learning, intelligent tutoring systems, virtual laboratories, simulation-based learning and generative AI can facilitate individualized learning and rapid feedback, although concerns regarding academic integrity, overdependence and inadequate AI literacy remain. In pharmaceutical research, AI can support target identification, molecular modelling, virtual screening, quantitative structure–activity relationship modelling, de novo molecular design, drug repurposing, toxicity prediction and clinical-trial optimization. However, recent evidence indicates that computational performance does not automatically translate into clinically meaningful drug-development outcomes, emphasizing the need for rigorous experimental validation and translational evaluation. In clinical pharmacy and patient care, AI-assisted prescription review, drug-interaction detection, medication management, clinical decision support, adherence monitoring and remote patient monitoring may improve efficiency and medication safety. AI also provides opportunities for pharmacovigilance by extracting adverse drug events from clinical narratives and integrating heterogeneous safety data. Responsible implementation therefore requires human oversight, transparent validation, appropriate governance and multidisciplinary collaboration. AI should be viewed as an augmentative technology that strengthens—not replace the scientific, clinical, ethical and humanistic responsibilities of pharmacists. Future pharmacy practice will increasingly require professionals who can critically evaluate AI outputs, understand data-driven systems and integrate computational recommendations with evidence-based pharmaceutical and clinical judgment.

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