IDEAS home Printed from https://ideas.repec.org/a/boh/actaub/v24y2021i1p19-35.html

Artificial Intelligence: Technology 4.0 as a solution for healthcare workers during COVID-19 pandemic

Author

Listed:
  • Anuj Kumar

    (Apeejay School of Management, Dwarka, Delhi)

  • Purvi Pujari

    (Bharati Vidyapeeth's Institute of Management Studies and Research, Navi Mumbai, Maharashtra, India)

  • Nimit Gupta

    (School of Management, The NorthCap University, Gurugram, India)

Abstract

The COVID-19 pandemic has brought unprecedented spotlight to the healthcare sector across the globe. It has revealed the weak links as well as neglected aspects of the healthcare sector. The aspects like data collection about patients, access to and outreach of authentic information in times of pandemics and use of technology for collaboration among medical researchers and workers have been overlooked for a long period of time. As the world is watching an inflection point of the digital technology era, it is highly imperative to analyze where these two supposedly separate sectors converge. This research paper has attempted to do the same. A systematic literature review of twenty research papers have been done to know impact of artificial intelligence in dealing with COVID-19 problem. The benefits offered by the technological revolution, namely technology 4.0, to the healthcare sector has been analyzed in this paper with a special emphasis on the role of Artificial Intelligence. Adoption of latest technology in collating patients handling systems, supply of medicines and other medical requirements, collaboration in the areas of research and development amid COVID-19 are few of the aspects of the paper. There is an immediate need for a technology embedded in healthcare system.

Suggested Citation

  • Anuj Kumar & Purvi Pujari & Nimit Gupta, 2021. "Artificial Intelligence: Technology 4.0 as a solution for healthcare workers during COVID-19 pandemic," Acta Universitatis Bohemiae Meridionalis, University of South Bohemia in Ceske Budejovice, Faculty of Economics, vol. 24(1), pages 19-35.
    • Handle: RePEc:boh:actaub:v:24:y:2021:i:1:p:19-35
    DOI: 10.32725/acta.2021.002
    as

    Download full text from publisher

    File URL: http://acta.ef.jcu.cz/doi/10.32725/acta.2021.002.html
    Download Restriction: free of charge
    File URL: http://acta.ef.jcu.cz/doi/10.32725/acta.2021.002.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.32725/acta.2021.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. da Silva, Ramon Gomes & Ribeiro, Matheus Henrique Dal Molin & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Higor Leite & Ian R. Hodgkinson & Thorsten Gruber, 2020. "New development: ‘Healing at a distance’—telemedicine and COVID-19," Public Money & Management, Taylor & Francis Journals, vol. 40(6), pages 483-485, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anuj Kumar & T. Sowdamini & Sanjay Manocha & Purvi Pujari, 2021. "Gamification as a Sustainable Tool for HR Managers," Acta Universitatis Bohemiae Meridionalis, University of South Bohemia in Ceske Budejovice, Faculty of Economics, vol. 24(2), pages 1-14.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Essam A. Rashed & Akimasa Hirata, 2021. "One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
    3. Yan, Tao & Wong, Pak Kin & Ren, Hao & Wang, Huaqiao & Wang, Jiangtao & Li, Yang, 2020. "Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    5. Barraza, Néstor Ruben & Pena, Gabriel & Moreno, Verónica, 2020. "A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Rohitash Chandra & Yixuan He, 2021. "Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    7. Nawin Raj, 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
    8. Sharov, Konstantin S., 2020. "Creating and applying SIR modified compartmental model for calculation of COVID-19 lockdown efficiency," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    9. Marcel Lucas Chee & Marcus Eng Hock Ong & Fahad Javaid Siddiqui & Zhongheng Zhang & Shir Lynn Lim & Andrew Fu Wah Ho & Nan Liu, 2021. "Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-15, April.
    10. Srinka Basu & Sugata Sen, 2023. "COVID 19 Pandemic, Socio-Economic Behaviour and Infection Characteristics: An Inter-Country Predictive Study Using Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 645-676, February.
    11. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    12. Sini V. Pillai & Ranjith S. Kumar, 2021. "The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: a review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 48(4), pages 375-389, December.
    13. Syed Kashif Badshah1Noor Badshah, 2024. "Classification of Medical Images Through Convolutional Neural Network Modification Method," International Journal of Innovations in Science & Technology, 50sea, vol. 6(5), pages 216-226, May.
    14. Yao, Haitang & Liu, Wei & Wu, Chia-Huei & Yuan, Yu-Hsi, 2022. "The imprinting effect of SARS experience on the fear of COVID-19: The role of AI and big data," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    15. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
    16. Wajdi Aljedaani & Eysha Saad & Furqan Rustam & Isabel de la Torre Díez & Imran Ashraf, 2022. "Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends," Mathematics, MDPI, vol. 10(17), pages 1-33, September.
    17. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    18. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    19. El-Sayed A El-Sherpieny & Ehab M Almetwally & Abdisalam Hassan Muse & Eslam Hussam, 2023. "Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-17, April.
    20. Wang, Lingxiao & Hare, Brian M. & Zhou, Kai & Stöcker, Horst & Scholten, Olaf, 2023. "Identifying lightning structures via machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boh:actaub:v:24:y:2021:i:1:p:19-35. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://edirc.repec.org/data/efjcucz.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.