Artificial Intelligence in Supply Chain Management: A Systematic Review of Emerging Trends and Evidence in Healthcare Operations

Abstract

The increasing complexity of healthcare supply chains characterized by fluctuating demand, stringent regulatory requirements, globalized procurement networks, and the critical need for real-time resource availability has accelerated the adoption of Artificial Intelligence (AI) as a transformative operational tool. This systematic review synthesizes emerging trends, empirical findings, and technological innovations in AI-enabled supply chain management within healthcare systems. Drawing on peer-reviewed literature from the past decade, the study examines how AI-driven techniques such as machine learning, predictive analytics, natural language processing, optimization algorithms, and intelligent automation enhance procurement forecasting, inventory management, logistics optimization, clinical resource allocation, and risk mitigation. The review highlights the growing integration of AI with enabling technologies such as digital twins, Internet of Medical Things (IoMT), blockchain, and cloud-based analytics to strengthen supply chain visibility, traceability, and resilience. Evidence shows that AI significantly reduces stock-outs, improves demand prediction accuracy, enhances cold-chain monitoring, and supports decision-making in critical service lines such as pharmaceuticals, surgical supplies, and emergency care. Despite these advancements, major challenges remain, including data fragmentation, interoperability limitations, model transparency concerns, workforce capacity gaps, and ethical issues relating to bias, privacy, and automation risks. The review concludes by outlining future research directions, emphasizing the need for explainable AI (XAI), scalable real-time analytics, integrated data governance frameworks, and hybrid human-AI decision architectures. This study provides a consolidated knowledge base for policymakers, healthcare administrators, and supply chain professionals seeking evidence-based pathways for AI adoption in healthcare operations.