Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Jun 24;11(7):1155.
doi: 10.3390/diagnostics11071155.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Nora El-Rashidy  1 , Samir Abdelrazik  2 , Tamer Abuhmed  3 , Eslam Amer  4 , Farman Ali  5 , Jong-Wan Hu  6 , Shaker El-Sappagh  7   8
Affiliations
Review

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Nora El-Rashidy et al. Diagnostics (Basel). .

Abstract

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Keywords: COVID_19; artificial intelligence; deep learning.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure A1
Figure A1
Distribution of infected people in terms of gender (male, female) among various countries.
Figure 1
Figure 1
Taxonomy of using AI in COVID-19.
Figure 2
Figure 2
Statistics between males and females based on the number of infected cases.
Figure 3
Figure 3
Drug repurposing based on AI techniques.
Figure 4
Figure 4
(A–E) subfigures show progression of a CT scan of a COVID-19 patient across days (2, 4, 5, 6, and 8, respectively).
Figure 5
Figure 5
(A–F) subfigures show progression of an X-ray image for a COVID-19 patient across days (1, 3, 6, 7, 8, and 10, respectively).
Figure 5
Figure 5
(A–F) subfigures show progression of an X-ray image for a COVID-19 patient across days (1, 3, 6, 7, 8, and 10, respectively).
Figure 6
Figure 6
(A–F) subfigures show the progression of a US image for a COVID-19 patient across days (1, 3, 6, 7, 8, and 10, respectively). The white arrows in each subfigure clarify the change in each day.
Figure 7
Figure 7
Types of textual datasets.
See this image and copyright information in PMC

References

    1. Fang S., Li K., Shen J., Liu S., Liu J., Yang L., Hu C.-D., Wan J. GESS: A database of global evaluation of SARS-CoV-2/hCoV-19 sequences. Nucleic Acids Res. 2021;49:D706–D714. doi: 10.1093/nar/gkaa808. - DOI - PMC - PubMed
    1. Ludwig S., Zarbock A. Coronaviruses and SARS-CoV-2: A Brief Overview. Anesth. Analg. 2020;20:6–9. doi: 10.1213/ANE.0000000000004845. - DOI - PMC - PubMed
    1. Bin S.Y., Heo J.Y., Song M.-S., Lee J., Kim E.-H., Park S.-J., Kwon H.-I., Kim S.M., Kim Y.-I., Si Y.-J., et al. Environmental Contamination and Viral Shedding in MERS Patients during MERS-CoV Outbreak in South Korea. Clin. Infect. Dis. 2015;62:755–760. doi: 10.1093/cid/civ1020. - DOI - PMC - PubMed
    1. Wang L.-F., Shi Z., Zhang S., Field H., Daszak P., Eaton B.T. Review of Bats and SARS. Emerg. Infect. Dis. 2006;12:1834–1840. doi: 10.3201/eid1212.060401. - DOI - PMC - PubMed
    1. Chen B., Tian E.-K., He B., Tian L., Han R., Wang S., Xiang Q., Zhang S., El Arnaout T., Cheng W. Overview of lethal human coronaviruses. Signal Transduct. Target. Ther. 2020;5:1–16. doi: 10.1038/s41392-020-0190-2. - DOI - PMC - PubMed

LinkOut - more resources