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Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients....

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Detalles Bibliográficos
Autores principales: Huang, Shigao, Yang, Jie, Fong, Simon, Zhao, Qi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071762/
https://www.ncbi.nlm.nih.gov/pubmed/33907522
http://dx.doi.org/10.7150/ijbs.58855
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author Huang, Shigao
Yang, Jie
Fong, Simon
Zhao, Qi
author_facet Huang, Shigao
Yang, Jie
Fong, Simon
Zhao, Qi
author_sort Huang, Shigao
collection PubMed
description Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic.
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spelling pubmed-80717622021-04-26 Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives Huang, Shigao Yang, Jie Fong, Simon Zhao, Qi Int J Biol Sci Review Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic. Ivyspring International Publisher 2021-04-10 /pmc/articles/PMC8071762/ /pubmed/33907522 http://dx.doi.org/10.7150/ijbs.58855 Text en © The author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Review
Huang, Shigao
Yang, Jie
Fong, Simon
Zhao, Qi
Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives
title Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives
title_full Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives
title_fullStr Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives
title_full_unstemmed Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives
title_short Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives
title_sort artificial intelligence in the diagnosis of covid-19: challenges and perspectives
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8071762/
https://www.ncbi.nlm.nih.gov/pubmed/33907522
http://dx.doi.org/10.7150/ijbs.58855
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