Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review
Artificial intelligence applications are known to facilitate the diagnosis and the treatment of COVID-19 infection. This research was conducted to investigate and systematically review the studies published on the use of artificial intelligence in the COVID-19 pandemic. The study was conducted betw...
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| Format: | Article |
| Language: | English |
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Tehran University of Medical Sciences
2022-08-01
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| Series: | Acta Medica Iranica |
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| Online Access: | https://acta.tums.ac.ir/index.php/acta/article/view/9422 |
| _version_ | 1811283655100727296 |
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| author | Gözde Özsezer Gülengül Mermer |
| author_facet | Gözde Özsezer Gülengül Mermer |
| author_sort | Gözde Özsezer |
| collection | DOAJ |
| description |
Artificial intelligence applications are known to facilitate the diagnosis and the treatment of COVID-19 infection. This research was conducted to investigate and systematically review the studies published on the use of artificial intelligence in the COVID-19 pandemic. The study was conducted between April 25 and May 6, 2020 by scanning national and international studies accessed in "Web of Science, Google Scholar, Pubmed and Scopus" databases with the keywords ("Coronavirus” or “COVID-19") and ("artificial intelligence" or “deep learning” or “machine learning”). As a result of the scanning process, 1495 (Google Scholar: 1400, Pubmed: 58, Scopus: 30, WOS: 7) studies were accessed. The studies were first examined according to their titles, and 1385 studies, which were not related to the research topic, were not included in the scope of the research. 50 articles, which did not meet the inclusion criteria, were excluded. The abstract and complete texts of the remaining 60 studies were scanned for the study's inclusion and exclusion criteria. A total of 10 studies, consisting of reviews, letters to the editor, meta-analysis studies, animal studies, conference presentations, studies not related to COVID-19, and incomplete studying protocols, were excluded. There were 50 studies left. 9 articles with duplication were identified and excluded. The remaining 41 studies were examined in detail. A total of 26 researches were found to meet the criteria for the systematic review study. In this systematic review, AI applications were found the be effective in COVID-19 diagnosis, classification, epidemiological estimates, mode of transmission, distribution and density of lesions, case increase estimation, mortality/mortality risk and early scans.
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| first_indexed | 2024-04-13T02:15:28Z |
| format | Article |
| id | doaj.art-fb9e0d90aa1c4f26a26e7d68a6cdd0fd |
| institution | Directory Open Access Journal |
| issn | 0044-6025 1735-9694 |
| language | English |
| last_indexed | 2024-04-13T02:15:28Z |
| publishDate | 2022-08-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Acta Medica Iranica |
| spelling | doaj.art-fb9e0d90aa1c4f26a26e7d68a6cdd0fd2022-12-22T03:07:10ZengTehran University of Medical SciencesActa Medica Iranica0044-60251735-96942022-08-0160710.18502/acta.v60i7.10208Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic ReviewGözde Özsezer0Gülengül Mermer1Department of Public Health Nursing, Faculty of Health Sciences, Çanakkale Onsekiz Mart University, Çanakkale, TurkeyDepartment of Public Health Nursing, Faculty of Nursing, Ege University, İzmir, Turkey Artificial intelligence applications are known to facilitate the diagnosis and the treatment of COVID-19 infection. This research was conducted to investigate and systematically review the studies published on the use of artificial intelligence in the COVID-19 pandemic. The study was conducted between April 25 and May 6, 2020 by scanning national and international studies accessed in "Web of Science, Google Scholar, Pubmed and Scopus" databases with the keywords ("Coronavirus” or “COVID-19") and ("artificial intelligence" or “deep learning” or “machine learning”). As a result of the scanning process, 1495 (Google Scholar: 1400, Pubmed: 58, Scopus: 30, WOS: 7) studies were accessed. The studies were first examined according to their titles, and 1385 studies, which were not related to the research topic, were not included in the scope of the research. 50 articles, which did not meet the inclusion criteria, were excluded. The abstract and complete texts of the remaining 60 studies were scanned for the study's inclusion and exclusion criteria. A total of 10 studies, consisting of reviews, letters to the editor, meta-analysis studies, animal studies, conference presentations, studies not related to COVID-19, and incomplete studying protocols, were excluded. There were 50 studies left. 9 articles with duplication were identified and excluded. The remaining 41 studies were examined in detail. A total of 26 researches were found to meet the criteria for the systematic review study. In this systematic review, AI applications were found the be effective in COVID-19 diagnosis, classification, epidemiological estimates, mode of transmission, distribution and density of lesions, case increase estimation, mortality/mortality risk and early scans. https://acta.tums.ac.ir/index.php/acta/article/view/9422COVID-19PandemicArtificial intelligenceDeep learningMachine learning |
| spellingShingle | Gözde Özsezer Gülengül Mermer Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review Acta Medica Iranica COVID-19 Pandemic Artificial intelligence Deep learning Machine learning |
| title | Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review |
| title_full | Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review |
| title_fullStr | Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review |
| title_full_unstemmed | Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review |
| title_short | Using Artificial Intelligence In The Covid-19 Pandemic: A Systematic Review |
| title_sort | using artificial intelligence in the covid 19 pandemic a systematic review |
| topic | COVID-19 Pandemic Artificial intelligence Deep learning Machine learning |
| url | https://acta.tums.ac.ir/index.php/acta/article/view/9422 |
| work_keys_str_mv | AT gozdeozsezer usingartificialintelligenceinthecovid19pandemicasystematicreview AT gulengulmermer usingartificialintelligenceinthecovid19pandemicasystematicreview |