Artificial Intelligence in Pharmacy: Revolutionizing Healthcare and Drug Development

In the pharmaceutical industry, artificial intelligence (AI) is a game-changer, transforming clinical decision-making, patient care, and drug development. Using generative models, deep learning, and machine learning, artificial intelligence (AI) improves pharmaceutical operations’ accuracy, efficiency, and cost-effectiveness. AI’s various applications in pharmacy are examined in this paper, which also describes its classifications by capabilities (Artificial Narrow Intelligence, General Intelligence, and Super Intelligence), functionalities (reactive machines, limited memory systems), and learning techniques (machine learning, deep learning). Important fields include drug research, where AI speeds up molecular design, virtual screening, and target identification, slashing development times from 14 years to months and drastically lowering costs. In clinical practice, AI helps with medication interaction monitoring, individualized treatment regimens through EHR data analysis, and illness diagnosis (e.g., Alzheimer’s prediction by MRI analysis, cancer detection in dermatology). AI also makes remote patient monitoring and telemedicine more efficient, allowing for early intervention and real-time health tracking. AI-powered technologies (like AlphaFold2 and DeepChem) and databases (like PubChem and DrugBank) help the pharmaceutical business by streamlining post-market surveillance, clinical trial optimization, and medication creation. AI is also used in specialist sectors including infectious diseases, cardiology, and cancer to enhance treatment results and diagnosis accuracy. Implementation issues are examined together with ethical issues and future possibilities, such as the potential of artificial general intelligence (AGI). Accelerated innovation, better patient outcomes, and sustainable healthcare solutions are achieved by the industry by incorporating AI into the whole drug lifecycle, from preclinical research to commercialization. This thorough analysis emphasizes how important AI is to bringing pharmacy into the data-driven, patient-centered era.