Edge-Cloud Integrated AI Framework for Real-Time Smart Healthcare Monitoring
DOI:
https://doi.org/10.5281/ijurd.v2i1.37Keywords:
Skin Cancer, Computer Vision, Dermoscopic Images, CNN, Medical DiagnosisAbstract
The increasing demand for real-time healthcare monitoring has led to the emergence of intelligent systems that combine edge and cloud computing capabilities. This paper presents an Edge-Cloud Integrated AI Framework for Real-Time Smart Healthcare Monitoring designed to enable efficient, low-latency, and scalable healthcare solutions. The proposed system leverages edge devices such as wearable sensors and IoT-enabled medical equipment to collect and process patient data locally, reducing latency and ensuring rapid response. Complex analytics and long-term storage are handled by cloud infrastructure, enabling comprehensive data analysis and model training. Machine learning and deep learning models are deployed across both edge and cloud layers to detect anomalies, predict health risks, and support clinical decision-making. The framework incorporates data security mechanisms including encryption and secure communication protocols to ensure patient privacy. Experimental results demonstrate that the proposed approach improves response time, reduces bandwidth usage, and enhances system reliability compared to purely cloud-based solutions. Additionally, integration with prior research in healthcare analytics strengthens system robustness and scalability. The study highlights the potential of edge-cloud integration in enabling efficient and intelligent real-time healthcare monitoring systems.
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Copyright (c) 2026 Anita Roy, Dev Yadav, Tanya Malhotra, Shweta Jain
This work is licensed under a Creative Commons Attribution 4.0 International License.
