Review question / Objective: The aim of this systematic review is to evaluate AI-based models in identifying predictors of cardiovascular events and risk predtion in patients with diabetes mellitus type2. Condition being studied: T2DM patients have an increased risk of macrovascular and microvascular complications, lead to decreased quality of life and mortality. Considering the significance of cardiovascular complications in these patients, prediction of such events would be important. Different traditional statistical methods(such as regression) and new AI-besed algorithms are used to predict these complications in diabetic patients.
Review question / Objective: The aim of this systematic review is to compare the accuracy of artificial intelligence algorithms with radiologist panels in CT interpretation in covid-19. Condition being studied: COVID-19 disease was reported as the cause of the outbreak of pneumonia at the end of 2019. One of the main complications of COVID-19 is pulmonary involvement which could be diagnosed by CT-scan dominantly. Because of the increasing rate of these patients along with considering patients in remote areas, CT interpretations are a heavy burden on radiologists. Therefore artificial intelligence algorithms have become critical and time-saving systems in decision-making for these patients.
Review question / Objective: The aim of this systematic review is to investigate the efficacy of mindfulness interventions on physiologic and psychological outcomes in heart disease. Condition being studied: Heart disease is one of the most important life-threatening conditions and various psychological and physical treatments are considered for them. one of the psychological interventions which is used widely in a chronic disease context, is mindfulness-based interventions that could be delivered online. Information sources: Web OF Science Core Collection; EMBASE; Scopus; Psych Info.
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