Leveraging Artificial Intelligence in Pharmacy and Clinical Pharmacy Transformative Innovations for Precision, Operational Efficiency, and Enhanced Patient-Centered Care - Asif, Laiba; Khan, Muhammad Danyal; Rafique, Maheen; Shaikh, Abdul Sami; Iman, Farwa; Mubeen, Misbah; Javaid, Ayesha; Jadoon, Gullelala; Zulfiqar, Maha; Shaikh, Mehwish (Indus Journal of Bioscience Research, 2025)
By improving accuracy, operational effectiveness, and patient-centered care, the use of artificial intelligence (AI) in clinical pharmacy and pharmacy is transforming healthcare. Drug discovery, customized medicine, and pharmaceutical treatment management are being optimized by AI-driven advances, including machine learning algorithms, natural language processing, and predictive analytics. AI-powered decision support systems in clinical pharmacies increase workflow efficiency, decrease adverse medication reactions, and improve prescription accuracy. Real-time patient monitoring, AI-assisted drug use evaluations, and automated dispensing robots all help to optimize resources and enhance therapeutic results. Additionally, AI improves drug adherence through digital health interventions such as chatbots, virtual assistants, and smartphone apps that offer instructional help and customized reminders. Drug safety surveillance is strengthened by the early detection of adverse drug responses made possible by the incorporation of AI in pharmacovigilance. Despite these developments, competent AI implementation requires addressing issues including data protection, legal compliance, and ethical considerations. This paper examines how artificial intelligence (AI) is changing pharmacy and clinical pharmacy, highlighting significant advancements, advantages, and difficulties, as well as how it might change the way healthcare is delivered. Pharmacists may adopt a more proactive, data-driven strategy by utilizing AI, which will eventually enhance patient safety, treatment results, and healthcare efficiency. To enable AI's responsible and successful incorporation into pharmacy practice, future research should concentrate on improving AI algorithms, encouraging multidisciplinary cooperation, and creating strong regulatory frameworks. To satisfy the changing needs of precision medicine and patient-centric healthcare, this study emphasizes the critical necessity for ongoing developments in AI-driven pharmacy solutions.