Telemedicine Framework in COVID-19 Pandemic

Abstract

The COVID-19 pandemic coincided with the growth and ripeness of several digital methods, such as Artificial Intelligence (AI) (including Machine Learning (ML) and Deep Learning (DL)), internet of things (IoT), big-data analytics, Software Defined Network (SDN), robotic technology, and blockchain, etc. resulting in an experience chance for telemedicine advancement. In several nations, a telemedicine platform based on digital technology has been built and integrated into the clinical workflow in a variety of modes, including many-to-one, one-to-many, consultation mode, and practical-operation modes. These platforms are practical, efficient, and successful for exchanging epidemiological data, facilitating face-to-face interactions between patients or healthcare professionals over long distances, lowering the risk of disease transmission, and enhancing patient outcomes. This article provides a Systematic Literature Review (SLR) to call attention to the most recent advancements in evaluating COVID-19 data utilizing various methodologies such as ML, DL, SDN, and IoT. The number of studies on ML and DL provided and reviewed in this article has proven a considerable effect on the prediction and spreading of COVID-19. The main goal of this study is to show how ML, DL, IoT, and SDN may be used by researchers to provide significant solutions for authorities and healthcare statements to lessen the influence of pestilence. This report also includes many novel strategies for raising the prevalent telemedicine use.

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