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Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
BACKGROUND AND OBJECTIVE: During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the p...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/ https://www.ncbi.nlm.nih.gov/pubmed/32834612 http://dx.doi.org/10.1016/j.chaos.2020.110059 |
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author | Lalmuanawma, Samuel Hussain, Jamal Chhakchhuak, Lalrinfela |
author_facet | Lalmuanawma, Samuel Hussain, Jamal Chhakchhuak, Lalrinfela |
author_sort | Lalmuanawma, Samuel |
collection | PubMed |
description | BACKGROUND AND OBJECTIVE: During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. METHOD: A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. RESULT: This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. CONCLUSION: The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic. |
format | Online Article Text |
id | pubmed-7315944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73159442020-06-25 Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review Lalmuanawma, Samuel Hussain, Jamal Chhakchhuak, Lalrinfela Chaos Solitons Fractals Review BACKGROUND AND OBJECTIVE: During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. METHOD: A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. RESULT: This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. CONCLUSION: The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic. Elsevier Ltd. 2020-10 2020-06-25 /pmc/articles/PMC7315944/ /pubmed/32834612 http://dx.doi.org/10.1016/j.chaos.2020.110059 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Review Lalmuanawma, Samuel Hussain, Jamal Chhakchhuak, Lalrinfela Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review |
title | Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review |
title_full | Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review |
title_fullStr | Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review |
title_full_unstemmed | Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review |
title_short | Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review |
title_sort | applications of machine learning and artificial intelligence for covid-19 (sars-cov-2) pandemic: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/ https://www.ncbi.nlm.nih.gov/pubmed/32834612 http://dx.doi.org/10.1016/j.chaos.2020.110059 |
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