Gaurav Pawar,
Ankita Chaudhari,
Nikhil Patil,
Akshata Girase,
G. M. Chavan,
- Research Scholar, SVS’s Dadasaheb Rawal Pharmacy College, Mandal Road, Dondaicha, Tal-Shindhkheda; Dist-Dhule;425408, Maharashtra, India
- Assistant Professor, Department of Pharmaceutics, SVS’s Dadasaheb Rawal Pharmacy College, Mandal Road, Dondaicha, Tal-Shindhkheda; Dist-Dhule;425408, Maharashtra, India
- Assistant Professor, Department of Pharmacology, SVS’s Dadasaheb Rawal Pharmacy College, Mandal Road, Dondaicha, Tal-Shindhkheda; Dist-Dhule;425408, Maharashtra, India
- Assistant Professor, Department of Quality Assurance, SVS’s Dadasaheb Rawal Pharmacy College, Mandal Road, Dondaicha, Tal-Shindhkheda; Dist-Dhule;425408, Maharashtra, India
- Principal, SVS’s Dadasaheb Rawal Pharmacy College, Mandal Road, Dondaicha, Tal-Shindhkheda; Dist-Dhule;425408, Maharashtra, India
Abstract
Artificial intelligence (AI) is transforming clinical pharmacy practice by enhancing the safety, accuracy, and efficiency of medication management while supporting evidence-based clinical decision-making. Recent advances in machine learning, deep learning, natural language processing, and predictive analytics have enabled the integration of AI into various aspects of pharmacy practice, including drug discovery, pharmacovigilance, clinical decision support, personalized medicine, medication adherence, and patient counselling. The objective of this review is to provide a comprehensive overview of current AI applications in clinical pharmacy, highlighting their role in optimizing drug therapy and improving patient care. A narrative review of the literature was conducted using published articles retrieved from major scientific databases, including PubMed, Scopus, and Web of Science, focusing on recent developments in AI-driven pharmaceutical care. The reviewed evidence indicates that AI technologies improve adverse drug reaction detection, facilitate individualized treatment strategies, enhance clinical trial efficiency, reduce medication errors, and support pharmacists in delivering patient-centred care. Furthermore, AI contributes to workflow optimization through automated prescription verification, inventory management, and predictive healthcare analytics. Despite these advancements, challenges related to data quality, algorithm transparency, ethical considerations, regulatory compliance, and patient privacy remain significant barriers to widespread implementation. Overall, AI has the potential to redefine the future of clinical pharmacy by complementing pharmacists’ expertise, improving healthcare outcomes, and promoting precision medicine. Continued interdisciplinary collaboration, robust regulatory frameworks, and high-quality clinical validation studies are essential to ensure the safe, effective, and responsible integration of AI into routine pharmacy practice.
Keywords: Artificial Intelligence; Clinical Pharmacy; Drug Therapy Optimization; Pharmacovigilance; Clinical Decision Support; Personalized Medicine; Patient Care; Machine Learning
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Research and Reviews: A Journal of Pharmacology
| Volume | 16 | |
| 03 | ||
| Received | 29/07/2026 | |
| Accepted | 08/09/2026 | |
| Published | 14/09/2026 | |
| Publication Time | 47 Days |