Jaideep Singh Bhullar
Vol. 15, Issue 1, Jan-Jun 2023
Abstract:
With the fast-paced growth of the Internet of Things (IoT) and Artificial Intelligence (AI), the healthcare industry is shifting to a paradigm of smart health monitoring systems with IoT-enabled medical devices and AI-driven analytics. These health monitoring systems, by combining the power of IoT and AI together in the healthcare value chain, help to improve real-time patient monitoring for early anomalies and allow personalized and timely interventions. IoT-enabled medical devices gather round-the-clock patient data: heart rate, oxygen saturation, glucose levels, ECG patterns, among others. The latter is transmitted via secure communication protocols to cloud-based or edge computing platforms, analyzed there by AI algorithms in search of abnormalities. The techniques used by AI include machine learning and deep learning for detecting anomalies and predicting health risks and for timely notification of health professionals to make proactive decisions. Although the advantages are numerous, there are also challenges such as data security, interoperability, computational efficiency, and regulatory compliance that hinder the widespread implementation of IoT-AI healthcare systems. Another significant concern is ensuring the reliability and accuracy of AI predictions. The solutions to these challenges lie in developing secure data transmission, federated learning, and energy-efficient AI models for real-time processing
DOI: http://doi.org/10.37648/ijrmst.v15i01.014