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

Marcel Lucas Chee, Marcus Eng Hock Ong, Fahad Javaid Siddiqui, Zhongheng Zhang, Shir Lynn Lim, Andrew Fu Wah Ho and Nan Liu
Additional contact information
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
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
Nan Liu: Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore

IJERPH, 2021, vol. 18, issue 9, 1-15

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.

Keywords: artificial intelligence; machine learning; COVID-19; emergency department; intensive care; critical care (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/18/9/4749/pdf (application/pdf)
https://www.mdpi.com/1660-4601/18/9/4749/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:18:y:2021:i:9:p:4749-:d:546359

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().