DISTRIBUTED AI IN HEALTHCARE: A CASE STUDY ON PANDEMIC RESPONSE
Keywords:
Distributed Healthcare AI, Pandemic Response Systems, Predictive Epidemiology, Vaccine Supply Chain Optimization, Healthcare Resource AllocationAbstract
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.
References
Mayur Suresh Gawande, et al, “The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development,” 2025 Jan, Available : https://pmc.ncbi.nlm.nih.gov/articles/PMC11695538/
Varisha Zuhair, et al, “Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations,” 2024, Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11010755/
Francesco Palmieri, et al, “A cloud-based architecture for emergency management and first responders localization in smart city environments,” November 2016, Available: https://www.sciencedirect.com/science/article/abs/pii/S0045790616300283
[Shalini Ramanathan, et al, “Edge Computing in Healthcare,” December 2023, Available : https://www.researchgate.net/publication/376742891_Edge_Computing_in_Healthcare
Sharif Noor Zisad, et al, “An Integrated Neural Network and SEIR Model to Predict COVID-19,” March 2021, Available : https://www.researchgate.net/publication/350174922_An_Integrated_Neural_Network_and_SEIR_Model_to_Predict_COVID-19
Sarthak Pati, et al, “Privacy preservation for federated learning in health care,” 12 July 2024, Available: https://www.sciencedirect.com/science/article/pii/S2666389924000825
George Christopher, et al, “Optimizing Vaccine Distribution Through AI-Driven Supply Chain Solutions,” October 2024, Available : https://www.researchgate.net/publication/384734085_Optimizing_Vaccine_Distribution_Through_AI-Driven_Supply_Chain_Solutions
Seyda Topaloglu Yildiz, et al, “Optimizing the COVID-19 cold chain vaccine distribution network with medical waste management: A robust optimization approach,” 1 November 2023, Available : https://www.sciencedirect.com/science/article/pii/S0957417423010126
Eline Sandvig Andersen, et al, “Monitoring performance of clinical artificial intelligence in health care: a scoping review,” 2024 Oct 17, Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC11630661/
Kathrin Blagec, et al, “Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals,” January 2023, Available : https://www.sciencedirect.com/science/article/pii/S1532046422002799
Betelhem Zewdu Wubineh, et al, “Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review,” March 2024, Available: https://www.sciencedirect.com/science/article/pii/S1078143923004179
Junaid Bajwa, et al, “Artificial intelligence in healthcare: transforming the practice of medicine,” . 2021 Jul, Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/
