ABSTRACT. In this article, I cumulate previous research findings indicating that Internet of Medical Things devices are instrumental in interconnected healthcare services and networks. I contribute to the literature on Internet of Medical Things in COVID-19 diagnosis, prognosis, and treatment by showing that monitoring systems and wearable sensors integrated in Internet of Medical Things and smart healthcare can assist patients remotely. Throughout February 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “COVID-19” + “networked wearable devices,” “machine learning-based real-time data sensing and processing,” and “Internet of Medical Things.” As I inspected research published between 2020 and 2022, only 159 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 34, generally empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, ROBIS, and SRDR.

Keywords: COVID-19; networked wearable device; Internet of Medical Things

How to cite: Balica, R.-Ș. (2022). “Networked Wearable Devices, Machine Learning-based Real-Time Data Sensing and Processing, and Internet of Medical Things in COVID-19 Diagnosis, Prognosis, and Treatment,” American Journal of Medical Research 9(1): 33–48. doi: 10.22381/ajmr9120223.

Received 27 February 2022 • Received in revised form 23 April 2022
Accepted 25 April 2022 • Available online 30 April 2022

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