DISTRIBUTED AI IN HEALTHCARE: A CASE STUDY ON PANDEMIC RESPONSE - Ankush Singhal, (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2025)
The global health crisis has catalyzed unprecedented advancements in healthcare through distributed artificial intelligence systems. This article examines the transformative impact of AI architectures across pandemic response efforts, focusing on epidemiological modeling, vaccine distribution, and resource allocation. The article details how cloud-based infrastructure and edge computing have revolutionized healthcare data processing, enabling real-time decision support and improved patient outcomes. The implementation of sophisticated predictive modeling combines traditional epidemiological approaches with neural networks, achieving significant improvements in outbreak prediction and resource optimization. The vaccine distribution system demonstrates how AI-driven supply chain management and multi-objective optimization algorithms have enhanced distribution efficiency and reduced wastage. The article also explores technical challenges, solutions, and future improvements in healthcare AI systems, highlighting the importance of privacy preservation and cross-border collaboration in global health crisis management.