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Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends
The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275905/ https://www.ncbi.nlm.nih.gov/pubmed/34276140 http://dx.doi.org/10.1007/s00530-021-00818-1 |
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author | Shah, Het Shah, Saiyam Tanwar, Sudeep Gupta, Rajesh Kumar, Neeraj |
author_facet | Shah, Het Shah, Saiyam Tanwar, Sudeep Gupta, Rajesh Kumar, Neeraj |
author_sort | Shah, Het |
collection | PubMed |
description | The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI’s flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19. |
format | Online Article Text |
id | pubmed-8275905 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-82759052021-07-14 Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends Shah, Het Shah, Saiyam Tanwar, Sudeep Gupta, Rajesh Kumar, Neeraj Multimed Syst Special Issue Paper The COVID-19 pandemic is rapidly spreading across the globe and infected millions of people that take hundreds of thousands of lives. Over the years, the role of Artificial intelligence (AI) has been on the rise as its algorithms are getting more and more accurate and it is thought that its role in strengthening the existing healthcare system will be the most profound. Moreover, the pandemic brought an opportunity to showcase AI and healthcare integration potentials as the current infrastructure worldwide is overwhelmed and crumbling. Due to AI’s flexibility and adaptability, it can be used as a tool to tackle COVID-19. Motivated by these facts, in this paper, we surveyed how the AI techniques can handle the COVID-19 pandemic situation and present the merits and demerits of these techniques. This paper presents a comprehensive end-to-end review of all the AI-techniques that can be used to tackle all areas of the pandemic. Further, we systematically discuss the issues of the COVID-19, and based on the literature review, we suggest their potential countermeasures using AI techniques. In the end, we analyze various open research issues and challenges associated with integrating the AI techniques in the COVID-19. Springer Berlin Heidelberg 2021-07-13 2022 /pmc/articles/PMC8275905/ /pubmed/34276140 http://dx.doi.org/10.1007/s00530-021-00818-1 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Special Issue Paper Shah, Het Shah, Saiyam Tanwar, Sudeep Gupta, Rajesh Kumar, Neeraj Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends |
title | Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends |
title_full | Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends |
title_fullStr | Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends |
title_full_unstemmed | Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends |
title_short | Fusion of AI techniques to tackle COVID-19 pandemic: models, incidence rates, and future trends |
title_sort | fusion of ai techniques to tackle covid-19 pandemic: models, incidence rates, and future trends |
topic | Special Issue Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275905/ https://www.ncbi.nlm.nih.gov/pubmed/34276140 http://dx.doi.org/10.1007/s00530-021-00818-1 |
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