Next-Gen Telehealth: A Low-Latency IoT and Edge AI Framework for Personalized Remote Diagnosis

This research introduces an enhanced IoT-based framework designed for real-time remote diagnosis and personalized telemedicine. The methodology integrates health-monitoring sensors, edge AI processing using Raspberry Pi 4 and NVIDIA Jetson Nano, and a secure cloud/telemedicine platform. Pre-trained AI models on the edge devices facilitate early detection and real-time alerts, while a smartphone gateway transmits data to the cloud. The cloud platform ensures secure data storage, EMR (Electronics Medical Record) integration, and real-time video consultations. A simulated smart healthcare environment was used for testing. Results demonstrate low latency (80-150ms) and high accuracy (92%) in edge AI inference. Data transmission efficiency was above 99.5%. A simulated patient usability survey indicated high satisfaction with the mobile health management app, and dashboards provided clear visualizations of patient data. The framework demonstrates potential for improved healthcare accessibility and efficiency compared to traditional telemedicine models.