ABSTRACT. This article presents an empirical study carried out to evaluate and analyze artificial intelligence-enabled wearable medical devices, clinical and diagnostic decision support systems, and Internet of Things-based healthcare applications in COVID-19 prevention, screening, and treatment. Building our argument by drawing on data collected from Accenture, GlobalWebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients’ vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Data collected from 6,200 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: wearable medical device; COVID-19; prevention; screening; treatment

How to cite: Barnes, R., and Zvarikova, K. (2021). “Artificial Intelligence-enabled Wearable Medical Devices, Clinical and Diagnostic Decision Support Systems, and Internet of Things-based Healthcare Applications in COVID-19 Prevention, Screening, and Treatment,” American Journal of Medical Research 8(2): 9–22. doi: 10.22381/ajmr8220211.

Received 12 May 2021 • Received in revised form 8 October 2021
Accepted 16 October 2021 • Available online 28 October 2021

Robin Barnes
This email address is being protected from spambots. You need JavaScript enabled to view it.
Real-Time Clinical Monitoring Laboratory
at ISBDA, Tucson, AZ, USA
(corresponding author)
Katarina Zvarikova
This email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Operation and Economics
of Transport and Communications,
Department of Economics,
University of Zilina, Zilina, Slovak Republic

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