The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

COVID-19 patients have presented with a wide range of neurological disorders, among which stroke is the most devastating. We have reviewed current studies, case series, and case reports with a focus on COVID-19 patients complicated with stroke, and presented the current understanding of stroke in this patient population. As evidenced by increased D-dimer, fibrinogen, factor VIII and von Willebrand factor, SARS-CoV-2 infection induces coagulopathy, disrupts endothelial function, and promotes hypercoagulative state. Collectively, it predisposes patients to cerebrovascular events. Additionally, due to the unprecedented strain on the healthcare system, stroke care has been inevitably compromised. The underlying mechanism between COVID-19 and stroke warrants further study, so does the development of an effective therapeutic or preventive intervention.