ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore smart wearable Internet of Medical Things technologies, artificial intelligence-based diagnostic algorithms, and real-time healthcare monitoring systems in COVID-19 detection and treatment. In this research, previous findings were cumulated showing that big data analytics can optimize healthcare services in Internet of Medical Things, and we contribute to the literature by indicating that data connectivity and sharing are pivotal in healthcare services. Throughout January 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “COVID-19” + “smart wearable Internet of Medical Things technologies,” “artificial intelligence-based diagnostic algorithms,” and “real-time healthcare monitoring systems.” As research published between 2020 and 2022 was inspected, only 127 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 31 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: Internet of Medical Things; diagnostic algorithm; COVID-19

How to cite: Crowell, B., Cug, J., and Frajtova Michalikova, K. (2022). “Smart Wearable Internet of Medical Things Technologies, Artificial Intelligence-based Diagnostic Algorithms, and Real-Time Healthcare Monitoring Systems in COVID-19 Detection and Treatment,” American Journal of Medical Research 9(1): 17–32. doi: 10.22381/ajmr9120222.

Received 29 January 2022 • Received in revised form 20 April 2022
Accepted 24 April 2022 • Available online 30 April 2022