Edge Artificial Intelligence

17Healthcare Applications of Edge AI

Shiwani Gupta*, Jagruti Jadhav, Shilpa Mathur, Sampada Bhonde and Pranjali Sankhe

AI&ML Department, Thakur College of Engineering and Technology, Mumbai, India

Abstract

The possible application of Edge AI in the absorption of healthcare provides enough improvement in conquering the limitations of centralized AI systems in environments, especially those that require real-time decision-making while promoting better data privacy. This chapter asks how Edge AI can transform diagnostics, personalized treatment, and streamline operations through the capability of AI estimation directly on devices such as wearable sensors. It also delves into the current challenges in healthcare, focusing on latency and data security issues, by highlighting the limitations of cloud-based AI systems, such as reliance on a stable internet connection and vulnerability to data breaches. Edge AI models are tested for processing speed, accuracy, and energy efficiency using a combination of literature analysis and experimental implementation. The results show 15% higher diagnostic accuracy and a 70% decrease in latency. Results are obvious where Edge AI is the dominant leader over the cloud-based solution with consistent real-time insights at lower operational costs. In conclusion, the chapter advocates the use of Edge AI in remote patient monitoring, emergency response, and its potential to revolutionize the delivery of healthcare by providing increased accessibility, ...