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

  1. Marcel Lucas Chee
  2. Marcus Eng Hock Ong
  3. Fahad Javaid Siddiqui
  4. Zhongheng Zhang
  5. Shir Lynn Lim
  6. Andrew Fu Wah Ho
  7. Nan Liu

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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.

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  1. Version published to 10.3390/ijerph18094749
  2. ScreenIT

    SciScore for 10.1101/2021.02.15.21251727: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.1 Search strategy and selection criteria: We searched six databases, PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library, by combining search terms related to AI, COVID-19, and intensive care or emergency settings.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This avoids artificial stratification of patients into risk groups and other limitations associated with the Hosmer-Lemeshow test 52. Studies should also endeavour to validate their data using stricter validation techniques. Studies with smaller sample sizes should utilise re-sampling techniques, such as bootstrapping or k-fold cross-validation. Studies with larger sample sizes should use a non-random split of data (e.g. by location or time) or perform external validation on independent data, for example, from a different study 22,23,55. Validation using the same data for model development is inappropriate as it only provides apparent model performance. Similarly, validation using a random split of data, such as a ‘train-test’ split, has lower power than re-sampling techniques 22,56 and should be avoided. In addition to the limitations in quality and reporting of AI applications, the narrow scope of applications being investigated naturally leads to fewer AI applications eventually being suitable for clinical use. While AI has been practically applied for the identification of candidate drugs for drug repurposing 57 and contact tracing 18, its application and utility for COVID-19 in clinical settings have been insignificant to date. Several studies have employed AI techniques for the detection and classification of COVID-19 images 20, however, none have been validated as a clinical diagnostic adjunct in the ED. Factors that may contribute to this lack of clinical validation i...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.02.15.21251727 on medRxiv