IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i9p4749-d546359.html
   My bibliography  Save this article

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review

Author

Listed:
  • Marcel Lucas Chee

    (Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria 3800, Australia)

  • Marcus Eng Hock Ong

    (Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore
    Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore)

  • Fahad Javaid Siddiqui

    (Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore)

  • Zhongheng Zhang

    (Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China)

  • Shir Lynn Lim

    (Department of Cardiology, National University Heart Centre, Singapore 119074, Singapore)

  • Andrew Fu Wah Ho

    (Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore
    Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore)

  • Nan Liu

    (Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore
    Health Service Research Centre, Singapore Health Services, Singapore 169856, Singapore
    Institute of Data Science, National University of Singapore, Singapore 117602, Singapore)

Abstract

Background : Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods : We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results : Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions : Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4749-:d:546359
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/9/4749/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/9/4749/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wenjuan Wang & Martin Kiik & Niels Peek & Vasa Curcin & Iain J Marshall & Anthony G Rudd & Yanzhong Wang & Abdel Douiri & Charles D Wolfe & Benjamin Bray, 2020. "A systematic review of machine learning models for predicting outcomes of stroke with structured data," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    2. Enzo Tartaglione & Carlo Alberto Barbano & Claudio Berzovini & Marco Calandri & Marco Grangetto, 2020. "Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data," IJERPH, MDPI, vol. 17(18), pages 1-17, September.
    3. Adrian Xi Lin & Andrew Fu Wah Ho & Kang Hao Cheong & Zengxiang Li & Wentong Cai & Marcel Lucas Chee & Yih Yng Ng & Xiaokui Xiao & Marcus Eng Hock Ong, 2020. "Leveraging Machine Learning Techniques and Engineering of Multi-Nature Features for National Daily Regional Ambulance Demand Prediction," IJERPH, MDPI, vol. 17(11), pages 1-15, June.
    4. 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).
    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. Meyer, Lea Mareen & Stead, Susan & Salge, Torsten Oliver & Antons, David, 2024. "Artificial intelligence in acute care: A systematic review, conceptual synthesis, and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 206(C).

    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. 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).
    2. Yuta Miyazaki & Michiyuki Kawakami & Kunitsugu Kondo & Masahiro Tsujikawa & Kaoru Honaga & Kanjiro Suzuki & Tetsuya Tsuji, 2023. "Improvement of predictive accuracies of functional outcomes after subacute stroke inpatient rehabilitation by machine learning models," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-14, May.
    3. Ching-Heng Lin & Ya-Wen Kuo & Yen-Chu Huang & Meng Lee & Yi-Wei Huang & Chang-Fu Kuo & Jiann-Der Lee, 2023. "Development and Validation of a Novel Score for Predicting Long-Term Mortality after an Acute Ischemic Stroke," IJERPH, MDPI, vol. 20(4), pages 1-12, February.
    4. 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.
    5. 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.
    6. 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.
    7. Josline Adhiambo Otieno & Jenny Häggström & David Darehed & Marie Eriksson, 2024. "Developing machine learning models to predict multi-class functional outcomes and death three months after stroke in Sweden," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-16, May.
    8. 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).
    9. Mohammad Reza Davahli & Krzysztof Fiok & Waldemar Karwowski & Awad M. Aljuaid & Redha Taiar, 2021. "Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks," IJERPH, MDPI, vol. 18(7), pages 1-12, April.
    10. Muhammad Aasem & Muhammad Javed Iqbal & Iftikhar Ahmad & Madini O. Alassafi & Ahmed Alhomoud, 2022. "A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning," Mathematics, MDPI, vol. 10(24), pages 1-29, December.
    11. Manuel Sánchez-Montañés & Pablo Rodríguez-Belenguer & Antonio J. Serrano-López & Emilio Soria-Olivas & Yasser Alakhdar-Mohmara, 2020. "Machine Learning for Mortality Analysis in Patients with COVID-19," IJERPH, MDPI, vol. 17(22), pages 1-20, November.
    12. Ehab M. Almetwally, 2022. "The Odd Weibull Inverse Topp–Leone Distribution with Applications to COVID-19 Data," Annals of Data Science, Springer, vol. 9(1), pages 121-140, February.
    13. Mario A Quiroz-Juárez & Armando Torres-Gómez & Irma Hoyo-Ulloa & Roberto de J León-Montiel & Alfred B U’Ren, 2021. "Identification of high-risk COVID-19 patients using machine learning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-21, September.
    14. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    15. Anil Babu Payedimarri & Diego Concina & Luigi Portinale & Massimo Canonico & Deborah Seys & Kris Vanhaecht & Massimiliano Panella, 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-11, April.
    16. Yoshihiko Kadoya & Somtip Watanapongvanich & Pattaphol Yuktadatta & Pongpat Putthinun & Stella T. Lartey & Mostafa Saidur Rahim Khan, 2021. "Willing or Hesitant? A Socioeconomic Study on the Potential Acceptance of COVID-19 Vaccine in Japan," IJERPH, MDPI, vol. 18(9), pages 1-18, May.
    17. Hebatullah Abdulazeem & Sera Whitelaw & Gunther Schauberger & Stefanie J Klug, 2023. "A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-25, September.
    18. 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.
    19. 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).
    20. 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).

    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:gam:jijerp:v:18:y:2021:i:9:p:4749-:d:546359. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.