The Prescription for Value and Efficiency: A Systematic Review of Artificial Intelligence in Optimizing Pharmacy Business and Drug Prices

Abstract

Background: Healthcare is facing unprecedented stress due to the threat of rising costs, drug shortages, and inefficiency in operations. Pharmacy operations, starting from the manufacturing plant to the patient's bedside, and the complex nexus of drug prices are important areas crying out for disruption. Artificial intelligence (AI), specifically machine learning (ML), natural language processing (NLP), and robotic process automation (RPA), is being viewed increasingly as a game-changing force that can help counter these problems. Aim: The aim of this systematic review is to integrate current literature and evidence on the application of AI for streamlining pharma operations and justifying drug cost. Methods: We systematically searched peer-reviewed articles, reports, and clinical trials published between 2010 and 2025. Results: The findings reflect that AI-based solutions are being used effectively across the value chain of pharma. In operations, AI systems streamline inventory management, automate dispensing, improve clinical decision support, and personalize medication adherence programs. In drug pricing, AI algorithms are transforming market access approaches, optimizing payer reimbursement contracts, and informing value-based pricing models using advanced analysis of real-world evidence (RWE). Although the potential is great, numerous challenges remain, including data privacy concerns, algorithmic bias, the "black box" issue, and regulatory challenges. Conclusion: The review opines that AI is not only an incremental but also a paradigm change towards improved, safer, and value-based pharmacy practice. Strategic investment, cross-disciplinary convergence, and robust regulatory frameworks are essential to unlock the full potential of AI in creating an enduring and patient-centric pharmaceutical system.

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