AI-Driven Advancements in Pharmacy: Enhancing Drug Discovery, Optimization and Clinical Decision-Making

Artificial Intelligence (AI) has emerged as a transformative force in pharmacy, reshaping drug discovery, medication management, and patient care. The integration of AI-driven methodologies, including machine learning, natural language processing (NLP), computer vision, and predictive analytics, has revolutionized pharmaceutical operations, enhancing efficiency, accuracy, and patient safety. AI-driven systems facilitate personalized medicine, clinical decision support, automated dispensing, and pharmacovigilance, thereby minimizing medication errors and optimizing treatment regimens. This paper explores the historical evolution, applications, benefits, and challenges associated with AI in pharmacy. The adoption of AI-driven predictive analytics aids in adverse drug reaction detection, patient risk stratification, and treatment optimization, while automated decision support systems enhance clinical accuracy and regulatory compliance. Deep learning and supervised learning models are extensively employed in drug discovery and development, significantly accelerating the identification of therapeutic candidates and repurposing existing medications. Moreover, AI-based inventory management and supply chain forecasting improve pharmaceutical logistics, reducing medication waste and ensuring optimal drug availability. Despite its vast potential, AI implementation in pharmacy is accompanied by ethical, regulatory, and financial challenges, including data privacy concerns, algorithmic bias, workforce displacement, and the need for continuous learning systems. The complexity of AI decision-making, particularly the "black box" problem, raises concerns regarding transparency and interpretability in clinical practice. Regulatory frameworks, such as GDPR and FDA guidelines, continue to evolve to address AI’s ethical and safety implications in pharmaceutical applications. As AI technology advances, its role in pharmacy will expand further, leading to improved medication safety, cost reduction, and enhanced patient engagement. By integrating supervised and unsupervised AI models, alongside IoT-driven monitoring systems, the pharmaceutical industry is poised to transition towards a more data-driven, predictive, and patient- centered approach to healthcare. This paper provides a comprehensive examination of AI’s current and potential applications in pharmacy, emphasizing the necessity for interdisciplinary collaboration, ethical AI governance, and ongoing research to fully harness its capabilities.