The use of quantum technologies opens up new opportunities for the development of medicines, improving the quality of diagnostics, protection of medical information and personal data, increasing the efficiency of medical decision-making. The aim of the research was to study the prospects of development and application of quantum technologies in the field of healthcare. In order to achieve the goal, the analysis of separate clusters of quantum technologies with maximum prospects of commercial application in healthcare was performed; the patent landscape of the considered technological area was built; a review of market products for healthcare created on the basis of quantum technologies was prepared. It is shown that quantum sensors, quantum computing and quantum-resistant cybersecurity solutions have received maximum development in healthcare. The number of created technical solutions in the technological field under consideration, which received patent protection, is more than 6.5 thousand, of which 3.5 thousand are supported.

As key beneficiaries of the use of quantum technologies in healthcare it is proposed to consider, first of all, pharmaceutical companies and biotechnology startups, which can reduce the time of modeling and testing of drugs, improve the accuracy of predicting side effects of drugs and drug-drug interactions through the use of quantum computing, accelerate the analysis of big data and optimize clinical trial protocols.

Literature data presented in open medical sources on the use of telemedicine in hematology were analyzed. Telemedicine is an effective way to manage and monitor patients in order to minimize in-person hospital visits when this can be avoided. The experience and perspectives of this type of interaction in terms of patient satisfaction and effectiveness in monitoring various hematologic diseases have been studied. Despite the small number of results with high evidence, the studies demonstrate an optimistic picture of the use of telemedicine in real clinical practice, which leads to the need for more large-scale and high-quality studies to introduce various forms of telemonitoring in the routine follow-up of hematologic patients.

Background. The need for effective health management requires the improvement of health statistics. Current methods of data collection are limited and inaccurate. The Digital Transformation Strategy until 2030 aims to create a secure and reliable health information infrastructure using domestic technologies.

Aim. To analyze the existing methods of collecting and analyzing medical statistics in different countries..

Methods. To obtain information, we searched for relevant studies published in eLibrary, Refseek, Virtual Learning Resources Center, Yandex and Googlе databases. The search strategy was based on such key words and word combinations in Russian and English as “statistics”, “collection”, “analysis”.

Results. The study identified key methods of development of medical statistics collection in Russia and worldwide, focusing on accuracy and completeness of data. The principles of confidentiality, coverage, quality, computability, regularity and representativeness were analyzed, as well as collection methods: surveys, continuous data collection and automated information transfer.

Conclusion. The uniqueness of the Russian system of statistical accounting in healthcare lies in the continuous registration of each case of disease in medical organizations. The introduction of modern digital solutions based on primary data is in line with the basic principles of statistics. This will simplify the work with information, increase its accuracy and accessibility for prompt response to changes in the healthcare sector.

Currently, artificial intelligence is one of the most rapidly developing areas of human knowledge. This topic is of great importance for science and practice, in general, and for medicine, in particular. Application of artificial intelligence technologies to the segmentation of brain areas and detection of abnormal areas is especially demanded and promising in the field of neurophysiology, neurosurgery, psychiatry, clinical psychology and other medical disciplines. This paper investigates existing methods for automated segmentation and analysis of data on the structure and functional state of the brain, as well as metrics used to evaluate the effectiveness of this approach.

Materials and methods. The work was performed using Systematic Mapping Study (SMS) methodology.

This study is limited to the subject area related to segmentation of brain areas and identification of abnormal areas in the brain.

Results. The main results of the study are presented in the form of classification tables and a mental map. It is shown that the purpose of the reviewed research is to improve accuracy in segmenting brain areas and finding abnormal areas. Such a metric as data processing time is used to evaluate the efficiency of the method for a small number of studies, and in most cases it is not considered at all. At the same time, the speed of image processing, depending on the method used, is measured in minutes, which significantly limits the possibility of using this approach in emergency situations, including life-threatening situations.

Conclusion. To analyze data on the structure and functional state of the brain in real time, modification of already developed methods of encephalic segmentation is required, as well as development of new, more efficient approaches. At the same time, the speed of data processing should be commensurate with the time of making an urgent conclusion about the state of the human brain.

The implementation of artificial intelligence in healthcare is a key direction for technology development in Russia, aimed at improving the quality of medical services and increasing diagnostic accuracy. However, the lack of standards for presenting metrics of diagnostic accuracy of artificial intelligence-based software (AI-based software) complicates comparative analysis and selection of the most suitable software for medical organizations. Therefore, developing a detailed classification of AI-based software is an important task for ensuring safety and quality of medical care, as well as determining the interchangeability of AI-based medical devices.

Purpose: This study aims to develop a clinical classification of AI-based software in radiology.

Materials and Methods: To conduct the study, a comprehensive analysis of available information on AI-based software in radiology was conducted using domestic and foreign databases. In the process of analysis, key aspects were identified, including clinical applicability of AI-based software, diagnostic accuracy of medical devices using AI in radiology.

Results: a clinical classification of AI-based software in the field of radiology was developed. In addition, an important observation regarding the representation of diagnostic accuracy metrics of AI-based software was identified. As a result, the proposed classification was extended and supplemented by defining the level of representation of diagnostic accuracy metrics depending on the clinical classification.

Conclusion: based on the conducted research, a clinical classification of AI-based software has been developed, which provides a unified approach to the presentation of data on diagnostic accuracy by developers. This approach improves the transparency and comparability of information about different AI-based software in medical practice, thereby improving the efficiency and safety of AI-based software use in medical practice. The results of this study have the potential to be scaled to other AI applications and can be used to improve the quality regulation system for AI-enabled medical devices.

Objective. To evaluate the promising application of neural networks for cephalometric analysis by analyzing the accuracy of manual identification of anatomical landmarks on digital lateral teleradiographs.

Materials and Methods. Markup of 100 anonymized teleradiographs in lateral projection by eleven orthodontists on 21 parameters was performed, 23100 digital X-ray images with a reference point plotted on them were obtained. The coordinates of the reference point were compared with the “base point”, i.e. the averaged coordinate for each reference point among all its localizations.

Results. According to the criterion of average deviation from the “base point”, the best accuracy was achieved for the apices of the incisal edges of the central incisors of the maxilla (is) (0.589, CI = 95%) and mandible (ii) (0.835, CI = 95%), as well as for the middle of the entrance to the Turkish saddle (S) (0.662, CI = 95%). For the group of landmarks with the lowest consistency, which included points such as Po (4.330, CI = 95%), Pt (2.999, CI = 95%) and Ba (2.887, CI = 95%), the use of artificial neural networks alone is likely to be insufficient to automate identifications and improve the quality of cephalometric analysis and other machine learning elements will need to be implemented.

Conclusion. Considering the results of our study, we can conclude that the proposed method demonstrates high accuracy for most points and can be used to automate cephalometric analysis with further development of machine learning technologies.

Background. Modern artificial intelligence algorithms provide new insights into potential risk factors and modeling tools that predict the chronic course of kidney disease in children. Management of chronic kidney disease (CKD) is based on the use of tools that help the physician to timely predict the transition from acute kidney disease to chronic kidney disease and timely refer the child to a nephrologist.

Aim. Тo develop a graphical tool to predict chronic kidney disease in children.

Methods. The initial data for the development of the graphic tool (nomogram) were our own results published earlier. High quality prognostic model (ROC-AUC>90%) was constructed based on predictors of chronic kidney disease in children that we identified previously (proteinuria, haematuria, IL4 gene C598T polymorphic marker).

Results. The constructed nomogram has a high prognostic value – with an accuracy of 98.9% to predict CKD in children.

Conclusion: The developed nomogram can be used as a graphical assistant for physicians to predict the chronic course of the disease in patients with acute kidney disease.