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Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases
Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, s...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224655/ https://www.ncbi.nlm.nih.gov/pubmed/37362659 http://dx.doi.org/10.1007/s11042-023-15247-7 |
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author | Panjeta, Manisha Reddy, Aryan Shah, Rushabh Shah, Jash |
author_facet | Panjeta, Manisha Reddy, Aryan Shah, Rushabh Shah, Jash |
author_sort | Panjeta, Manisha |
collection | PubMed |
description | Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods. |
format | Online Article Text |
id | pubmed-10224655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-102246552023-05-30 Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases Panjeta, Manisha Reddy, Aryan Shah, Rushabh Shah, Jash Multimed Tools Appl Article Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods. Springer US 2023-05-27 /pmc/articles/PMC10224655/ /pubmed/37362659 http://dx.doi.org/10.1007/s11042-023-15247-7 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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 | Article Panjeta, Manisha Reddy, Aryan Shah, Rushabh Shah, Jash Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases |
title | Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases |
title_full | Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases |
title_fullStr | Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases |
title_full_unstemmed | Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases |
title_short | Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases |
title_sort | artificial intelligence enabled covid-19 detection: techniques, challenges and use cases |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224655/ https://www.ncbi.nlm.nih.gov/pubmed/37362659 http://dx.doi.org/10.1007/s11042-023-15247-7 |
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