A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine
Year: 2022
Type: review
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.
Authors: John Shen, Siddharth Ghatti, Nate Ryan Levkov, Haiying Shen, Tanmoy Sen, Karen Rheuban, Kyle Enfield, Nikki Reyer Facteau, Gina Engel, Kim Dowdell
Institutions: University of Virginia, University of Virginia Health System
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,150
OpenAlex ID: W4311111541