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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. [ABSTRACT FROM AUTHOR]

The aim of this paper is to synthesize and analyze existing evidence on virtual care technologies, wearable health monitoring sensors, and Internet of Medical Things-based smart disease surveillance systems in the diagnosis and treat- ment of COVID-19 patients. Using and replicating data from Deloitte, Ericsson ConsumerLab, GlobalWebIndex, McKinsey, PwC, Sony, and Sykes, we performed analyses and made estimates regarding artificial intelligence-based diagnostic algo- rithms in Internet of Things-supported healthcare delivery. Artificial intelligenceenabled wearable medical devices, virtualized care systems, and wireless biomedical sensing devices are pivotal in COVID-19 screening, testing, and treatment. Digital epidemiological surveillance in monitoring, detection, and prevention of COVID-19 is optimized by use of medical artificial intelligence, clinical and diagnostic decision support systems, machine learning-based real-time data sensing and processing, and smart healthcare devices and applications. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]