Authors: Sagar Gupta, Vikas kumar
Abstract: Hospitals often face immense challenges related to resource utilization and managing these resources efficiently in light of increasing demands from patients and the volume of data. The use of traditional centralized healthcare computing systems introduces latency and inefficiencies related to real-time decision-making. This chapter reviews how Edge AI can transform hospital resource utilization. By processing data closer to its source using edge devices, Edge AI allows for real-time analytics, proactive resource allocation, and responsiveness of operations. This chapter details the current challenges in hospital resource management, the architecture of an Edge AI-driven resource management system, and also discusses the case studies for their implementation. Quantitative evaluation regarding improved performances such as reduced wait time for patients and improvement in the bed occupancy rate is discussed. Integration of Edge AI with IoT and other emerging technologies such as 5G and federated learning is also considered as future work. Our analysis further shows that Edge AI increases not only hospital efficiency but also better patient outcomes through intelligent and timely interventions.
