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https://doi.org/10.3390/ijerph18094749
Title: | Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review | Authors: | Chee, Marcel Lucas Ong, Marcus Eng Hock Siddiqui, Fahad Javaid Zhang, Zhongheng Lim, Shir Lynn Ho, Andrew Fu Wah Liu, Nan |
Keywords: | Science & Technology Life Sciences & Biomedicine Environmental Sciences Public, Environmental & Occupational Health Environmental Sciences & Ecology artificial intelligence machine learning COVID-19 emergency department intensive care critical care MECHANICALLY VENTILATED PATIENTS MULTIVARIABLE PREDICTION MODEL INDIVIDUAL PROGNOSIS DIAGNOSIS TRIPOD VALIDATION EXPLANATION CALIBRATION SIMULATION EVENTS RISK |
Issue Date: | 1-May-2021 | Publisher: | MDPI | Citation: | Chee, Marcel Lucas, Ong, Marcus Eng Hock, Siddiqui, Fahad Javaid, Zhang, Zhongheng, Lim, Shir Lynn, Ho, Andrew Fu Wah, Liu, Nan (2021-05-01). Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 18 (9). ScholarBank@NUS Repository. https://doi.org/10.3390/ijerph18094749 | 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 under-score the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic. | Source Title: | INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | URI: | https://scholarbank.nus.edu.sg/handle/10635/228743 | ISSN: | 16617827 16604601 |
DOI: | 10.3390/ijerph18094749 |
Appears in Collections: | Staff Publications Elements |
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