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A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19.
Islam, Md Mohaimenul; Poly, Tahmina Nasrin; Alsinglawi, Belal; Lin, Ming Chin; Hsu, Min-Huei; Li, Yu-Chuan Jack.
  • Islam MM; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan.
  • Poly TN; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110301, Taiwan.
  • Alsinglawi B; Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 110301, Taiwan.
  • Lin MC; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110301, Taiwan.
  • Hsu MH; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110301, Taiwan.
  • Li YJ; Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei 110301, Taiwan.
J Clin Med ; 10(9)2021 May 02.
Article in English | MEDLINE | ID: covidwho-1224038
ABSTRACT
Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Observational study / Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Jcm10091961

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Observational study / Prognostic study Language: English Year: 2021 Document Type: Article Affiliation country: Jcm10091961