AI and distributed manufacturing systems: Strengthening healthcare supply chains for national biosecurity

Abstract

This article examines how artificial intelligence and decentralized technologies can transform healthcare supply chains to enhance national biosecurity. A comprehensive framework integrating predictive analytics, autonomous logistics, and distributed manufacturing is presented to create resilient healthcare ecosystems capable of withstanding pandemics, geopolitical conflicts, and cyber threats. Long Short-Term Memory networks and reinforcement learning algorithms offer unprecedented capabilities for demand forecasting and resource allocation, while Graph Neural Networks optimize medical distribution routes with improved efficiency. Blockchain technology provides tamper-proof transparency throughout pharmaceutical supply chains, and additive manufacturing enables localized production of critical supplies during disruptions. Digital twin simulations allow healthcare organizations to anticipate potential shortages before they materialize. Implementation challenges include data interoperability barriers, infrastructure limitations in developing regions, algorithmic bias risks, and data privacy concerns, all of which can be addressed through standardized exchange formats, coordinated investment strategies, formal fairness assessments, and federated learning approaches.