Exploring Potential Applications of Artificial Intelligence Technologies in Medicine Cold Supply Chains: A Systematic Literature Review

Purpose: The pharmaceutical cold supply chain (PCSC) ensures temperature-sensitive medicines like vaccines and biologics stay safe and effective, but failures like temperature deviations and inefficient distribution cause major financial losses and health risks. This systematic literature review (SLR) investigates the potential applications of artificial intelligence (AI) technologies in medicine cold supply chains adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework. Methodology: 2,418 were retrieved and analyzed sixty-eight peer-reviewed articles published between January 2015, and February 2026 were retrieved from Scopus, Web of Science, IEEE Xplore, ScienceDirect, PubMed, and Emerald Insight. Findings: Reviewed studies concentrated on high-income economies, with limited empirical validation in low- and middle-income countries (LMICs) where cold chain failures are most acute. Few studies report longitudinal performance, generalizability across multiple drug classes, or cost-benefit metrics suitable for procurement decisions. Unique Contribution to Theory, Policy and Practice: Theoretically, unlike prior reviews focused on food cold chains or generic supply chain AI, this paper provides the first dedicated PRISMA-guided synthesis exclusively addressing AI in the medicine cold supply chain and proposes an integrated research agenda anchored in resilience, equity, sustainability, and explainability. For policymakers, it highlights the need for harmonized data standards, regulatory sandboxes, and equity-focused deployment to extend AI benefits to vulnerable populations. For practitioners, the review offers an evidence-based taxonomy to guide AI investment prioritization across temperature monitoring, predictive maintenance, routing, and end-to-end visibility.