Author(s)
Marcel Lucas Chee
Published 4 Projects
COVID-19 Infectious Diseases Health Informatics Abstract Emergency Medicine
Marcus Eng Hock Ong
Published 4 Projects
COVID-19 Infectious Diseases Health Informatics Abstract Emergency Medicine
Fahad Javaid Siddiqui
Published 2 Projects
COVID-19 Infectious Diseases Abstract Artificial Intelligence Machine Learning
Zhongheng Zhang
Published 1 Project
COVID-19 Infectious Diseases Abstract Artificial Intelligence Machine Learning
Shir Lynn Lim
Published 1 Project
COVID-19 Infectious Diseases Abstract Artificial Intelligence Machine Learning
Andrew Fu Wah Ho
Published 4 Projects
COVID-19 Infectious Diseases Health Informatics Abstract Emergency Medicine
Nan Liu
Published 4 Projects
COVID-19 Infectious Diseases Health Informatics Abstract Emergency Medicine
Content
Video Abstract (AI generated) (02:15) Paper PreprintBackground: 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, focusing on methods, reporting standards, and clinical utility. 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 using PROBAST (prediction model risk of bias assessment tool) and a modified TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) statement for AI. We 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. Studies had low adherence to reporting guidelines, with particularly poor reporting on model calibration and blinding of outcome and predictor assessment. Of the remaining three studies, two evaluated the prognostic utility of deep learning-based lung segmentation software and one studied an AI-based system for resource optimisation in the ICU. These studies had similar issues in methodology, validation, and reporting. 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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Marcel Lucas Chee. (2021, Oct 29).Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review[Video]. Scitok. https://scitok.com/project/p/3e69283d
Lucas Chee Marcel. "Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review" Scitok, uploaded by Lucas Chee Marcel, 29 Oct, 2021, https://scitok.com/project/p3e69283d
Marcel Lucas Chee. "Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review" Scitok. (Oct 29, 2021). https://scitok.com/project/p/3e69283d
Marcel Lucas Chee (Oct 29, 2021). Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review Scitok. https://scitok.com/project/p/3e69283d
Marcel Lucas Chee. Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review[video]. 2021 Oct 29. https://scitok.com/project/p/3e69283d
@online{al2006link, title={ Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review }, author={ Lucas Chee, Marcel }, organization={Scitok}, month={ Oct }, day={ 29 }, year={ 2021 }, url = {https://scitok.com/project/p/3e69283d}, }