AI-Enabled Personalized Healthcare Monitoring in IoT Environments
Abstract
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has unlocked unprecedented possibilities in the realm of personalized healthcare monitoring. This research paper delves into the innovative integration of AI techniques within IoT environments to cater to the burgeoning need for individualized healthcare solutions.
The proposed framework harnesses the capabilities of AI algorithms to interpret and analyze voluminous healthcare data generated by IoT devices, ensuring tailored and real-time monitoring of an individual’s health parameters. Leveraging machine learning models, such as deep neural networks and predictive analytics, facilitates the continuous evaluation of vital signs, medication adherence, activity patterns, and other pertinent health metrics.
Furthermore, this paper scrutinizes the amalgamation of edge computing with AI in IoT ecosystems, enabling localized data processing and swift decision-making while maintaining data privacy and security. A comprehensive examination of existing technologies, their applicability, limitations, and ethical considerations in deploying AI-driven healthcare monitoring in IoT environments is undertaken.
The findings underscore the transformative potential of AI-enabled personalized healthcare monitoring in IoT ecosystems, shedding light on its implications for improved patient outcomes, healthcare efficiency, and the broader societal impact on the future of healthcare delivery.
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