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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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).
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    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).

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