ABSTRACT. We develop a conceptual framework based on a systematic and comprehensive literature review on virtualized care systems, medical artificial intelligence, and real-time clinical monitoring in COVID-19 diagnosis, screening, surveillance, and prevention. Building our argument by drawing on data collected from Accenture, AIR, Amwell, Ericsson ConsumerLab, Ginger, Kyruus, PwC, and Syneos Health, we performed analyses and made estimates regarding how connected wearable biomedical devices can assist in configuring precise diagnoses. Internet of Medical Things carries out real-time clinical monitoring by use of artificial intelligence-based diagnostic algorithms. Machine learning algorithms assist in diagnosis, screening, surveillance, and prevention. The data for this research were gathered via an online survey questionnaire. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: virtualized care; medical artificial intelligence; COVID-19 diagnosis

How to cite: Walters, M., and Kalinova, E. (2021). “Virtualized Care Systems, Medical Artificial Intelligence, and Real-Time Clinical Monitoring in COVID-19 Diagnosis, Screening, Surveillance, and Prevention,” American Journal of Medical Research 8(2): 37–50. doi: 10.22381/ajmr8220213.

Received 18 May 2021 • Received in revised form 21 October 2021
Accepted 24 October 2021 • Available online 28 October 2021

Mark Walters
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Center for Automated Digital Systems
at CLI, Las Vegas, NV, USA
(corresponding author)
Eva Kalinova
This email address is being protected from spambots. You need JavaScript enabled to view it.
The School of Expertness and Valuation,
The Institute of Technology and Business
in Ceske Budejovice, Czech Republic

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