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.
【저자키워드】 COVID-19, Critical care, intensive care, artificial intelligence, machine learning, emergency department, 【초록키워드】 COVID-19 pandemic, diagnostic, database, improvement, healthcare, Digital, Clinical management, methodology, health system, prognostic, Care, COVID-19 patients, Evidence, Safe, high risk, Predictive, Participants, CINAHL, handling, library, sample sizes, predictive models, poor quality, effective, current, common, ACM, Result, applied, facilitate, searched, peer-reviewed, eligible, were assessed, 【제목키워드】 review, artificial, application, Emergency, intelligence,