A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

John Shen; Siddharth Ghatti; Nate Ryan Levkov; Haiying Shen; Tanmoy Sen; Karen Rheuban; Kyle Enfield; Nikki Reyer Facteau; Gina Engel; Kim Dowdell

doi:10.3389/frai.2022.1034732

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine

Front Artif Intell. 2022 Dec 2:5:1034732. doi: 10.3389/frai.2022.1034732. eCollection 2022.

Authors

Affiliations

¹ Department of Computer Science, University of Virginia, Charlottesville, VA, United States.
² School of Medicine, University of Virginia, Charlottesville, VA, United States.
³ University of Virginia (UVA) Health System, University of Virginia, Charlottesville, VA, United States.

Abstract

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

Keywords: AI; COVID-19 detection; IoT; mobile devices; telemedicine.

Publication types

Review