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Extensive spread of the coronavirus disease (COVID-19) prompted an investigation of its diagnostic features. Acute viral pneumonia associated with COVID-19 has been described in detail using CT, radiography, and MRI. There is no data in the literature on the descriptive picture observed with dynamic MRI. Considering a comprehensive diagnostic approach, radiologists should know how to correctly recognize and interpret COVID-19 on MRI. This case series demonstrated the ability of dynamic MRI to detect the cloudy sky sign and distinguish it from consolidation in COVID-19 patients, thus presumably distinguishing between early or mild changes and a progressive clinical course. These changes in dynamic lung images on MRI can be recorded depending on the phase of the respiratory cycle. Thus, MRI, as a radiation-free tool that can be used to examine a patient with acute viral pneumonia COVID-19, can be useful in cases where access to computed tomography is limited and dynamic morphofunctional imaging is required.

Speech recognition technology is a promising tool for healthcare systems. This technology has a fairly long history of use in US and European healthcare systems, beginning in the 1970s. However, it only became widespread at the beginning of the 21st century, replacing medical transcriptionists. For Russian healthcare system, speech recognition technology is a new tool. Its active development began only in the early 2010s, and its active implementation in healthcare began in the late 2010s. Such a delay is due to the peculiarities of the Russian language and the limitation of computing power present at the beginning of the XXI century. Currently, speech recognition technology is used to fill out medical reports by voice, it allows you to reduce the preparation time of the protocol of radiological studies in comparison with the traditional (keyboard) text input. The review described a brief history of the development and application of speech recognition technology in radiology. Key scientific studies confirming the effectiveness of its use in US and European healthcare systems are described. Our experience in the use of speech recognition technology is demonstrated and its effectiveness is evaluated. The prospects for further development and application of this technology in Russian health care are described.

The issues of quality medical care management and organization of the work of the department of radiation diagnostics are always relevant and require constant monitoring and analytical expertise. Since 2018, the Moscow regional branch of the Russian Society of Radiologists and Radiologists (MRO PORR) has been conducting an independent assessment of the departments of radiation diagnostics in all the regions of Russia. The rating aimed to identify industry leaders and spread the best practices throughout the country. The survey results identified the positive trends in the development of diagnostic care services throughout the country and critical points that affect the quality of work of medical organizations. This study presents an analysis of the functioning of 123 departments of radiation diagnostics in 2020. After meeting the inclusion criteria, a list of 163 medical organizations in 15 cities of 7 federal districts was formed. The evaluation procedure was divided into three stages. The first stage consisted of an online survey, wherein each of the participating organizations was asked to answer questions about the departments work arrangement, equipment, list, and features of performing diagnostic tests, as well as working with patients. The second stage consisted of a clinical and technical audit of a set of anonymized studies with conclusions. Special attention was paid to technical audits since several medical organizations violated the methodology of conducting research. The third stage included checking the information about medical organizations in open sources. During the first and second stages, points were awarded, based on which the finalists, leaders, and rating winners were selected. According to the evaluation results of all stages,31 organizations reached the final stage,6 were in the group of leaders, and 5 were winners, whereas 45% of the finalists belonged to the Central Federal District. Greater interest was found in the auditing work in municipal and private medical institutions than in departmental and federal ones. Some database has been collected, in addition to the list of winners, which may represent a cross-section of the state of the radiation diagnostics service in the Russian Federation. Conducting such competitions is primarily aimed at improving the quality and safety of X-ray examinations. The methodology of the competition is improved every year.

Modern medical imaging methods allow for both qualitative and quantitative evaluations of tumors and issues surrounding them. Advances in computer science and big data processing are transforming any radiological study into analytic datasets, especially with the use of machine learning in medical image analysis. Among these datasets, statistically significant correlations with clinical events can then be searched for to subsequently assess their predictive value and ability to predict a particular clinical outcome. This concept, known as radiomics, was first described in 2012. It is particularly important in oncology because each type of tumor can be subdivided into many different molecular genetic subtypes, and simple visual characteristics are no longer sufficient. Moreover, as an absolutely noninvasive method, radiomics can provide a radiologist with additional information that would otherwise be unavailable without a histological examination of biopsy material. However, as with any methodology based on the use of big data, the question of the quality of the initial data becomes critical, because this can directly affect the outcome of the analysis and provide incorrect diagnostic information. In this literature review, we examine potential approaches to ensuring the quality of research at all stages, from technical control of the state of diagnostic equipment to the extraction of imaging markers in oncology and the calculation of their correlation with clinical data.