Integration of Deep Learning Applications and IoT for Smart Healthcare

Abstract

The integration of deep learning (DL) applications with the Internet of Things (IoT) has emerged as a transformative approach for advancing smart healthcare systems. This review synthesizes findings from seven research studies, each exploring the intersection of these technologies in improving healthcare delivery, patient monitoring, and medical decision-making. The paper highlights how IoT devices, including sensors and wearables, generate vast amounts of real-time health data, which DL models leverage for predictive analytics, diagnosis, and personalized treatment recommendations. Key areas explored include: Data Acquisition and Processing: IoT-enabled sensors play a critical role in collecting physiological data, such as heart rate, blood pressure, and glucose levels, which are then processed by DL algorithms to identify patterns and anomalies, Remote Patient Monitoring: The combination of IoT and DL facilitates continuous monitoring of chronic conditions and allows for real-time intervention, reducing hospital readmissions and enhancing patient independence.