The concept of the metaverse is a new, actively developing idea with potential in various fields of medicine. The combination of multisensory stimulation and mutual interaction opens up a wide range of possibilities for the application of metaverse therapy technologies in the context of a pandemic. The aim of this review is to analyze the scope of application and development of the metaverse perspective in the context of a crisis. Methods: The authors searched PubMed, ScienceDirect using the keyword "metaverse", among which a manual search was conducted for studies related to various aspects of rehabilitation. Of the 1393 publications found, 37 were selected for further analysis. Results: Metaverse technologies are used in medical rehabilitation, helping to restore physical and cognitive functions. Creating digital twins-avatars and using machine learning to process patient data can make rehabilitation more personalized and effective. Discussion: The concept of the metaverse creates a unique environment based on the synergy of high technology and social interaction. The new opportunities offered by the use of the metaverse in medicine can radically change rehabilitation, making it more effective and accessible.

Machine learning algorithms are used in many areas of medicine. Prenatal screening (PS) is no exception. Implementing machine learning techniques to evaluate PS results can help overcome the problems inherent in human analysis: reduce subjectivity and inter-expert variability when reading medical images, reduce examination time, and stratify pregnant women into risk groups with greater reliability. The scoping review was conducted to evaluate the diagnostic performance of machine learning technologies in PS. Twenty-seven relevant papers were identified by through PubMed, Cochrane and eLibrary databases. All included papers demonstrated the potential of machine learning methods to detect, classify, or predict of the risk of congenital anomalies. Interpreting medical images, machine learning allows to reduce the diagnostic time, improve its quality, ensure screening performance in remote areas or in conditions of staff shortage and to maintain sufficient sensitivity and specificity, regardless of the doctor's qualifications. Algorithms based on metabolomic analysis have advantages in accuracy and efficiency in predicting chromosomal anomalies. Clinical decision support systems based on factors of anamnesis and results of prenatal diagnostics can improve the prediction of congenital anomalies in the first trimester of pregnancy, both in terms of screening accuracy and in reducing the cost of the screening program. However, current evidence is mainly derived from the implementation of machine learning systems with low autonomy, and the authors of most of the studies included in the analysis describe a number of limitations that must be taken into account when implementing such solutions.

The implementation of the medical decision support system (MDSS) in clinical practice requires careful monitoring to ensure patient safety and track the performance of artificial intelligence technologies.
Goal: To assess the effectiveness of the TOP-3 MDSS in outpatient clinics of the Moscow Heath Care Department.
Materials and methods: The Moscow Heath Care Department monitored the TOP-3 operation between October 1, 2020 and March 03, 2024 (n = 63,809,360 people). The Hit-3 metric was used to determine if the MDSS needs retraining. An additional study involving medical experts included a retrospective analysis of data from 3,000 patients and calculation of the diagnostic agreement rate among the MDSS, doctors and the experts.
Results: The monitoring demonstrated mean Hit-3 of 63.5, 64.5 and 67.7 for MDSS ver. 1, 2, and 3, respectively. In cases where the clinicians disagreed with the MDSS (n = 2000), based on the patient complaints, experts agreed with the MDSS in 80.2% of cases, with clinicians in 11.5% of cases, and established a different diagnosis in 8.3% of cases. In cases where the clinicians’ and the MDSS’s conclusions matched (n = 1000), experts approved the diagnosis in 50.4% of cases, selected an alternative diagnosis suggested by the MDSS in 37.9% of cases, and established a different diagnosis for 11.7% of patients. Conclusion: The described monitoring methodology, supplemented by expert review, allowed for a comprehensive assessment of the MDSS to be implemented in the health care system. Based on the results of the TOP-3 effectiveness assessment, it was decided that the analyzed list of data from electronic health records should be expanded, which will be implemented in the upcoming version TOP-3+.

The aim of the study: development of a screening method for patients aimed at early differential diagnosis of malignant skin neoplasms using dermatoscopy in combination with optoelectronic mobile equipment and algorithms for classifying dermatoscopic images based on machine learning methods.
Materials and methods. To implement the detection of malignant neoplasms and classify them into the appropriate nosological group, machine learning methods, algorithms and optical recognition are used. The latter is used in the process of forming dermatoscopic images and training classification algorithms and models. The machine learning approaches are multi-class and binary cascade two-stage classification methods by classification algorithms based on the visual transformer architecture and neural network architecture.
Results. During the experimental evaluation of the results of multi-class classification (eight types of malignant neoplasms), the best classification model with the visual transformer architecture was determined, characterized by the metrics Accuracy of 0.932 and F-measure of 0.891 on the formed dataset, including ISIC-2019 and our own set containing 657 images. The binary cascade two-stage classification for melanocytic and non-melanocytic neoplasms has Accuracy and F-measure values — of 0.954 and 0.948 (the first stage of classification) and for melanomas and nevi — 0.964 and 0.951, respectively (the second stage of classification).
Conclusion. The obtained quantitative values of the malignant skin neoplasms detection accuracy by the developed screening examination method allow us to recommend the introduction of a multi-class classification for the primary division of a large volume of dermatoscopic images patients by nosological sign between medical specialists in the process of conducting mass (visiting) preventive examinations, and the introduction of a cascade binary classification in the an initial appointment conditions with limited access to specialized specialists to differentiate melanoma from other skin neoplasms. The developed screening examination method for patients can be introduced into medical practice as a system for supporting physician decision-making.

Large generative models (LGMs) have significant potential for healthcare and medical science. While publications are growing exponentially, LGM studies lack quality and breakthrough findings. Research articles call for standardized approaches to ensure safe and effective integration of LGMs into clinical practice. Currently, the Moscow healthcare system is testing LGMs as tools for supporting medical decision-making, which has required development of specialized methods and techniques for assessing LGM quality. This paper presents two methods for assessing the quality of large generative models. Both methods are based on analysis of literature data (over 200 sources), results from comprehensive testing of 204 LGMs, and hands-on experience in assessing model quality using a sample of more than 12,000 cases. Designed for two main LGM application scenarios, the methods incorporate a dedicated approach to building test samples, tailored and validated questionnaires, testing methodologies, and unified requirements for the composition and structure of quality assessment outputs.

Digital transformation of healthcare requires effective tools for assessing the digital maturity of medical organizations. This study is aimed at developing a methodology for assessing digital maturity adapted to the specifics of the health care system of the Russian Federation. The work includes the analysis of regulatory legal acts, identification of key criteria for assessing digital maturity, grouped into blocks, and development of an algorithm for calculating the level of digital maturity of medical organizations. The proposed methodology provides the ability to objectively assess, identify problem areas and develop recommendations for improving the digital maturity of organizations.
Objective. To develop a methodology for assessing the digital maturity of medical organizations, which takes into account the specifics of the healthcare system of the Russian Federation, allows for a comprehensive and objective assessment of the level of digital maturity and the formation of recommendations to improve the processes of digital transformation.
Materials and methods. A working group of 14 experts with experience in healthcare and digital transformation was formed to develop the methodology. Regulatory legal acts and existing approaches to digital maturity assessment were analyzed. Based on the expert survey, key criteria grouped into five blocks were identified. Indicators and calculation algorithms were developed for each block, which ensures objectivity, transparency and the possibility of automating the assessment.
Results. The developed methodology for assessing the digital maturity of medical organizations allows to objectively determine the level of their readiness for digital transformation. It covers the main aspects of digitalization, providing a comprehensive approach to analysis. The methodology also makes it possible to identify key problems that hinder digital transformation and form recommendations for their elimination. This makes it an effective tool for increasing the level of digital maturity of healthcare organizations and improving the quality of services provided.
Conclusion. The developed methodology for assessing the digital maturity of medical organizations is a universal tool for objective and systematic assessment of the level of digital maturity. It takes into account the peculiarities of the structure and activities of organizations, ensuring adaptability to different conditions and levels of healthcare. The methodology contributes to the standardization of digital transformation, identification of problem areas and formation of individual recommendations for their elimination.

In the context of digitalization of healthcare, the development of modern information systems for collecting and processing medical statistics is essential. This article discusses the development and presents a comprehensive analysis of functional requirements for these systems, viewing them as complex technological solutions that combine strict regulatory standards, advanced digital technologies, and the practical needs of health care organizations. Special attention is given to data quality assurance methodology, principles of integrating these systems with existing IT infrastructures and creation of conditions for analytical work based on collected statistics.