Virtual Care Technologies, Wearable Health Monitoring Sensors, and Internet of Medical Things-based Smart Disease Surveillance Systems in the Diagnosis and Treatment of COVID-19 Patients

Abstract

Digital epidemiological surveillance in monitoring, detection, and prevention of COVID-19 is optimized by use of medical artificial intelligence, clinical and diagnostic decision support systems, machine learning-based real-time data sensing and processing, and smart healthcare devices and applications. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Pustokhina et al., 2020) Body sensor networks integrate interconnected bio-sensors and wearable healthcare devices (Kovacova and Lăzăroiu, 2021;Lyons and Lăzăroiu, 2020) that assess abnormal alterations in vital physiological signs and share medical imaging data for patient diagnosis and monitoring, being instrumental in chronic diseases by use of deep learning-based applications. Conclusions, Implications, Limitations, and Further Research Directions Artificial intelligence-enabled wearable medical devices, virtualized care systems, and wireless biomedical sensing devices are pivotal in COVID-19 screening, testing, and treatment.