International Journal of Science, Engineering and TechnologyAuthors: Eshaan Kale
Abstract: The rapid evolution of digital technologies has catalyzed a paradigm shift in healthcare, moving from reactive clinical visits to proactive, continuous monitoring. This review article explores the convergence of the Internet of Things (IoT), Cloud Computing, and Artificial Intelligence (AI) into a unified framework designed for smart healthcare monitoring and clinical decision support. The integration begins at the perception layer, where an array of medical sensors and wearable devices facilitates the real-time acquisition of physiological data. To address the challenges of high data volume and time-sensitive alerts, we examine the role of edge and fog computing in reducing latency before data is transmitted to the cloud. Central to this framework is the application of AI and Machine Learning (ML) algorithms, which transform raw biometric streams into actionable insights. We provide a detailed analysis of various AI methodologies, including Deep Learning for medical imaging and Recurrent Neural Networks for time-series vitals prediction, which form the backbone of modern Decision Support Systems (DSS). Furthermore, the review identifies key application domains such as chronic disease management, elderly care, and post-operative telerehabilitation. Despite the promising potential of these integrated systems, several bottlenecks persist. We critically evaluate challenges related to data security, patient privacy, and the interoperability of heterogeneous medical devices. Finally, the article discusses emerging trends such as Digital Twins and Explainable AI (XAI), providing a roadmap for future research. This review serves as a comprehensive resource for researchers and practitioners aiming to design resilient, scalable, and intelligent healthcare infrastructures that improve patient outcomes while reducing the burden on traditional medical facilities.
DOI: https://doi.org/10.5281/zenodo.18221501
