Artificial intelligence technology for diagnosing COVID-19 cases: a review of substantial issues
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Alsharif, M. H. | - |
dc.contributor.author | Alsharif, Y. H. | - |
dc.contributor.author | Chaudhry, S. A. | - |
dc.contributor.author | Albreem, M. A. | - |
dc.contributor.author | Jahid, A. | - |
dc.contributor.author | Hwang, E. | - |
dc.date.accessioned | 2021-08-31T16:07:23Z | - |
dc.date.available | 2021-08-31T16:07:23Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 1128-3602 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/59005 | - |
dc.description.abstract | Today, the world suffers from the rapid spread of COVID-19, which has claimed thousands of lives. Unfortunately, its treatment is yet to be developed. Nevertheless, this phenomenon can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. In this study, the early diagnosis of this disease through artificial intelligence (Al) technology is explored. Al is a revolutionizing technology that drives new research opportunities in various fields. Although this study does not provide a final solution, it highlights the most promising lines of research on Al technology for the diagnosis of COVID-19. The major contribution of this work is a discussion on the following substantial issues of Al technology for preventing the severe effects of COVID-19: (1) rapid diagnosis and detection, (2) outbreak and prediction of virus spread, and (3) potential treatments. This study profoundly investigates these controversial research topics to achieve a precise, concrete, and concise conclusion. Thus, this study provides significant recommendations on future research directions related to COVID-19. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | VERDUCI PUBLISHER | - |
dc.title | Artificial intelligence technology for diagnosing COVID-19 cases: a review of substantial issues | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, E. | - |
dc.identifier.doi | 10.26355/eurrev_202009_22875 | - |
dc.identifier.scopusid | 2-s2.0-85091587499 | - |
dc.identifier.wosid | 000569313100081 | - |
dc.identifier.bibliographicCitation | EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES, v.24, no.17, pp.9226 - 9233 | - |
dc.relation.isPartOf | EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES | - |
dc.citation.title | EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES | - |
dc.citation.volume | 24 | - |
dc.citation.number | 17 | - |
dc.citation.startPage | 9226 | - |
dc.citation.endPage | 9233 | - |
dc.type.rims | ART | - |
dc.type.docType | Review | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Pharmacology & Pharmacy | - |
dc.relation.journalWebOfScienceCategory | Pharmacology & Pharmacy | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Coronavirus pandemic | - |
dc.subject.keywordAuthor | Al | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Big data | - |
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