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Background and Aims: Due to the COVID‐19 pandemic, a precise and reliable diagnosis of this disease is critical. The use of clinical decision support systems (CDSS) can help facilitate the diagnosis of COVID‐19. This scoping review aimed to investigate the role of CDSS in diagnosing COVID‐19. Methods: We searched four databases (Web of Science, PubMed, Scopus, and Embase) using three groups of keywords related to CDSS, COVID‐19, and diagnosis. To collect data from studies, we utilized a data extraction form that consisted of eight fields. Three researchers selected relevant articles and extracted data using a data collection form. To resolve any disagreements, we consulted with a fourth researcher. Results: A search of the databases retrieved 2199 articles, of which 68 were included in this review after removing duplicates and irrelevant articles. The studies used nonknowledge‐based CDSS (n = 52) and knowledge‐based CDSS (n = 16). Convolutional Neural Networks (CNN) (n = 33) and Support Vector Machine (SVM) (n = 8) were employed to design the CDSS in most of the studies. Accuracy (n = 43) and sensitivity (n = 35) were the most common metrics for evaluating CDSS. Conclusion: CDSS for COVID‐19 diagnosis have been developed mainly through machine learning (ML) methods. The greater use of these techniques can be due to their availability of public data sets about chest imaging. Although these studies indicate high accuracy for CDSS based on ML, their novelty and data set biases raise questions about replacing these systems as clinician assistants in decision‐making. Further studies are needed to improve and compare the robustness and reliability of nonknowledge‐based and knowledge‐based CDSS in COVID‐19 diagnosis. [ABSTRACT FROM AUTHOR]

Introduction: Advances in mobile health have led to numerous relevant studies in diagnosis, treatment, and controlling of various diseases. One of the criteria indicating the quality of the previously published studies is the number of citations. Therefore, investigating the features of highly cited articles and identifying the most frequently used mobile technological interventions can affect future research ideas. This study aimed at identifying 100 highly cited interventional studies on mobile health, type of used mobile technologies, and effect of these technologies in various diseases in top-cited articles. Material and Methods: The database employed in this study was the Web of Science, which without limitations was analysed in April 2020 to identify 100 highly cited interventional studies in the field of mobile health. The identified studies were classified based on the number of citations, year of publication, country of the first author, type of disease, and use of mobile technology. Results: A great majority of the studies in the field of interventional mobile health focused on obesity (n=18), addiction (n=15), diabetes (n=13) and mental health disorders (n=12), respectively. Many studies employed mobile technologies to promote lifestyle (weight loss and increased physical activity) (n=20), disease controls (n=20), and treatment adherence (n=18). The mean number of citations per study was 146±97. The most cited study was in the category of viral disease treatment adherence (n=703), and the most cited articles were published in 2012. Conclusion: Among the reviewed 100 studies, many of the interventional studies regarding mobile health focused on obesity, addiction, diabetes and mental health disorders. Promotion of lifestyle, disease controls, and treatment adherence were effects of mobile technologies in top-cited articles. Text messaging service was used as intervention in most of the studies. Thus, future studies may focus on the use of various mobile applications on different diseases’ prevention, control, and treatment. [ABSTRACT FROM AUTHOR]