ABSTRACT. The purpose of this study is to examine wearable medical sensor devices, machine and deep learning algorithms, and Internet of Things-based healthcare systems in COVID-19 patient screening, diagnosis, monitoring, and treatment. In this article, I cumulate previous research findings indicating that artificial intelligence tools can predict COVID-19 transmission patterns, assess disease severity, and predict mortality rate. I contribute to the literature on mobile medical applications and technologies by showing that Internet of Medical Things can save COVID-19 diagnosis time and optimize physiological patient data collection by medical sensor devices. Throughout January 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “COVID-19” + “wearable medical sensor devices,” “machine and deep learning algorithms,” and “Internet of Things-based healthcare systems.” As I inspected research published between 2019 and 2022, only 144 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 28, 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, MMAT, and SRDR.

Keywords: Internet of Things; wearable medical sensor device; COVID-19

How to cite: Jenkins, T. (2022). “Wearable Medical Sensor Devices, Machine and Deep Learning Algorithms, and Internet of Things-based Healthcare Systems in COVID-19 Patient Screening, Diagnosis, Monitoring, and Treatment,” American Journal of Medical Research 9(1): 49–64. doi: 10.22381/ajmr9120224.

Received 26 January 2022 • Received in revised form 22 April 2022
Accepted 27 April 2022 • Available online 30 April 2022

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