Abstract
Empirical evidence on artificial intelligence-powered diagnostic tools, networked medical devices, and cyber-physical healthcare systems in assessing and treating patients with COVID-19 symptoms has been scarcely documented in the literature. (Tsikala Vafea et al., 2020) Internet of Medical Things necessitates the deployment of health data from wearable mobile healthcare and smart sensing devices and applications networked across electronic health records in clinical and diagnostic decision support and remote healthcare systems. (Williams Samuel et al., 2020) COVID-19 detection and monitoring systems can acquire instantaneous symptom data from artificial intelligence-enabled wearable medical devices, identifying potential COVID-19 cases by use of machine learning algorithms. 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.
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