AI-Driven Remote Patient Monitoring: Enhancing Home-Based Healthcare with IoT and Machine Learning
DOI:
https://doi.org/10.52783/jns.v14.2878Keywords:
Artificial Intelligence, Internet of Things, Remote Patient Monitoring, Home-Based Healthcare, Machine Learning, Cloud Computing, Edge Computing, Data SecurityAbstract
The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized remote patient monitoring, enabling continuous, real-time health assessments in home-based settings. This paper explores the current landscape of AI-driven remote patient monitoring systems, focusing on how IoT devices and machine learning algorithms enhance healthcare delivery. We discuss the architecture of these systems, including the roles of cloud, fog, and edge computing, and address challenges such as data security, patient privacy, and system interoperability. Through a comprehensive review of recent studies, we highlight the effectiveness of AI in early disease detection, personalized care, and reducing hospital readmissions. The findings underscore the potential of AI and IoT to transform home-based healthcare, offering insights into future research directions and practical implementations.
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