ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore wearable Internet of Medical Things sensor devices, big healthcare data, and artificial intelligence-based diagnostic algorithms in real-time COVID-19 detection and monitoring systems. Using and replicating data from Accenture, Deloitte, The Economist, eMarketer, Gartner, GE Healthcare, Ginger, McKinsey, MIT Technology Review Insights, PwC, R2G, SSCG Media Group, Statista, and Sykes, we performed analyses and made estimates regarding the integration of connected wearable medical devices and clinical data. Real-time COVID-19 symptom data can be gathered by use of wearable sensors placed on the patient’s body. Smart data fusion from Internet of Things devices is adequate when diagnosing COVID-19 primarily. Wearable medical devices are instrumental in remote health monitoring systems. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

Keywords: Internet of Medical Things; COVID-19; big healthcare data; sensor

How to cite: Turner, D., and Pera, A. (2021). “Wearable Internet of Medical Things Sensor Devices, Big Healthcare Data, and Artificial Intelligence-based Diagnostic Algorithms in Real-Time COVID-19 Detection and Monitoring Systems,” American Journal of Medical Research 8(2): 132–145. doi: 10.22381/ajmr82202110.

Received 14 May 2021 • Received in revised form 12 October 2021
Accepted 15 October 2021 • Available online 28 October 2021

Daniel Turner
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The Cognitive Labor Institute,
New York City, NY, USA
(corresponding author)
Aurel Pera
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University of Craiova, Romania

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