Marcel Lucas Chee1, Marcus Eng Hock Ong2,3, Fahad Javaid Siddiqui2, Zhongheng Zhang4, Shir Lynn Lim5, Andrew Fu Wah Ho2,3, Nan Liu2,6,7. 1. Faculty of Medicine, Nursing and Health Sciences, Monash University, Victoria 3800, Australia. 2. Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore. 3. Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore. 4. Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China. 5. Department of Cardiology, National University Heart Centre, Singapore 119074, Singapore. 6. Health Service Research Centre, Singapore Health Services, Singapore 169856, Singapore. 7. 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.
Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19patients. 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-19patients, 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.
Authors: Nan Liu; Feng Xie; Fahad Javaid Siddiqui; Andrew Fu Wah Ho; Bibhas Chakraborty; Gayathri Devi Nadarajan; Kenneth Boon Kiat Tan; Marcus Eng Hock Ong Journal: JMIR Res Protoc Date: 2022-03-25