Abstract
With the frequent speedily rise in the number of recently reported and suspected cases of COVID-19, COVID-19 is a significant threat to public health, cultural, social and foreign relations around the world. Accurate diagnosis has to turn into a critical issue affecting the containment of this disease, especially at the countries which outbreak the virus. In the fight against COVID-19, Artificial Intelligence (AI) techniques have played a significant role in many aspects. In this chapter, a systematics review of the recent work related to COVID-19 containment using AI and big data techniques is introduced, showing their main findings and limitations to make it easy for researchers to investigate new techniques that will help the healthcare sector worker and reduce the spread of COVID-19 pandemic. The chapter also presents the problems and challenges and present to the researchers and academics some future research points from the AI point of view that can help healthcare sectors and curbing the COVID-19 spread.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
McCall, Becky: COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit. Health 2(4), e166–e167 (2020)
Haleem, A., Javaid, M., Vaishya, R.: Effects of COVID 19 pandemic in daily life. Curr. Med. Res. Pract. (2020). https://doi.org/10.1016/j.cmrp.2020.03.011
Vaishya, R., Javaid, M., Khan, I.H., Haleem, A.: Artificial Intelligence (AI) applications for COVID-19 pandemic. Diab. Metab. Syndr. Clin. Res. Rev. 14, 337–339 (2020)
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Cha, Y., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements and prognosis of COVID-19 pneumonia using computed tomography. Cell
Apostolopoulos, I.D., Mpesiana, T.A.: Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks Phys. Eng. Sci. Med. 43, 635–640 (2020)
Cobb, J.S., Seale, M.A.: Examining the effect of social distancing on the compound growth rate of SARS-CoV-2 at the county level (United States) using statistical analyses and a random forest machine learning model. Public Health 185, 27–29 (2020)
Sheikh, J.A., Singh, J., Singh, H., Jamal, S., Khubaib, M., Kohli, S., Dobrindt, U., Rahman, S.A., Ehtesham, N.Z., Hasnain, S.E.: Emerging genetic diversity among clinical isolates of SARS-CoV-2: lessons for today. Infect. Genet. Evol. 84, 104330 (2020)
WHO: Coronavirus disease 2019 (COVID-19) Situation Report, 96 (2020). https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200425-sitrep-96-covid-19.pdf?sfvrsn=a33836bb_2. Accessed 25 Apr 2020
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
Ting, D.S.W., Liu, Y., Burlina, P., et al.: AI for medical imaging goes deep. Nat. Med. 24, 539–540 (2018). https://doi.org/10.1038/s41591-018-0029-3
Narin, A., Kaya, C., Pamuk, Z.: Automatic Detection of Coronavirus Disease (Covid-19) Using X-Ray Images and Deep Convolutional Neural Networks. arXiv preprint arXiv:2003.10849 (2020)
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., Shen, D.: Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev. Biomed. Eng. (2020)
Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., et al.: Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia. arXiv preprint arXiv:2002.09334 (2020)
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shi, Y.: Lung Infection Quantification of Covid-19 in CT Images with Deep Learning. arXiv preprint arXiv:2003.04655 (2020)
Shi, F., Xia, L., Shan, F., Wu, D., Wei, Y., Yuan, H., Jiang, H., Gao, Y., Sui, H., Shen, D.: Large-Scale Screening of Covid-19 from Community Acquired Pneumonia Using Infection Size-Aware Classification.” arXiv preprint arXiv:2003.09860 (2020)
Bai, H.X., Hsieh, B., Xiong, Z., Halsey, K., Choi, J.W., Tran, T.M., Pan, I., Shi, L.B., Wang, D.C., Mei, J., Jiang, X.L.: Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology (2020). https://doi.org/10.1148/radiol.2020200823
Hu, Z., Ge, Q., Jin, L., Xiong, M.: Artificial Intelligence Forecasting of COVID-19 in China. arXiv preprint arXiv:2002.07112 (2020)
Imran, A., Posokhova, I., Qureshi, H.N., Masood, U., Riaz, S., Ali, K., John, C.N., Nabeel, M.: AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples Via an App. arXiv preprint arXiv:2004.01275 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hamdy, W., Darwish, A., Hassanien, A.E. (2021). Artificial Intelligence Strategy in the Age of Covid-19: Opportunities and Challenges. In: Hassanien, A.E., Darwish, A. (eds) Digital Transformation and Emerging Technologies for Fighting COVID-19 Pandemic: Innovative Approaches. Studies in Systems, Decision and Control, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-63307-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-63307-3_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63306-6
Online ISBN: 978-3-030-63307-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)