From Theory to Practice: Real-World Implementation of Artificial Intelligence and Machine Learning in Pharmacy Settings

This literature review examines the applications and implications of artificial intelligence (AI) and machine learning (ML) across three key pharmaceutical settings: community pharmacy, hospital pharmacy, and the pharmaceutical industry over the past five years. Based on a comprehensive analysis of electronic databases, including detailed case studies and implementation analyses from major healthcare institutions, the review demonstrates significant improvements in healthcare delivery. Key findings include substantial reductions in medication errors, improvements in patient adherence, and considerable cost savings across implementations. In community pharmacies, AI systems improved medication adherence and patient engagement. Hospital implementations enhanced clinical decision support and automated dispensing systems. In pharmaceutical industry settings, AI accelerated drug discovery processes and optimizes supply chain management. While implementation challenges include high costs, technical infrastructure requirements, and regulatory compliance, emerging technologies such as quantum computing and federated learning show promise for future applications. The review emphasizes balancing technological innovation with patient-centered care.