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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. [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]

This article presents an empirical study carried out to evaluate and analyze artificial intelligence-enabled wearable medical devices, clinical and diag- nostic decision support systems, and Internet of Things-based healthcare applications in COVID-19 prevention, screening, and treatment. Building our argument by drawing on data collected from Accenture, GlobalWebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients’ vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Data collected from 6,200 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]