Preview

Medical Doctor and Information Technologies

Advanced search

Journal “Medical doctor and information technologies” is the only Russian journal publishing research articles on medical information technologies. The journal is indexed in Higher Attestation Commission database, containing journals publishing main results of PhD thesis.

Journal “Medical Doctor and Information Technology” is an official indexed journal of the N.I. Pirogov National Medical Surgical Center.

The journal "Medical Doctor and Information Technologies" has a steady role of a navigator in the field of IT solutions, and is intended to demonstrate the variety of possibilities for applying modern methods and approaches to the medical data collection, processing and analysis.

Recently, the number of studies in our complex and interesting field of science, combining medicine and information technology has been continuously increasing.

The journal spreads up-to-date research about new areas of digital healthcare, artificial intelligence, medical decision support systems, block chain in healthcare, informatization projects in Russian regions, terminology, standardization, educational information technologies, diagnostic systems, mathematical modeling and other topics.

Current issue

No 2 (2025)
View or download the full issue PDF (Russian)

REVIEWS

6-15 87
Abstract

   All medical devices, both in the Russian Federation and in the world, undergo registration procedures. However, the related rules and legislation are regulated differently.

   The purpose of this study was to assess the functionality of the existing legal framework and registration systems for medical devices in some countries of the world.

ORIGINAL RESEARCH

16-31 58
Abstract

   The demand for specialists with profound knowledge in the subject areas of both the medical profile and information technology has led to a significant increase in the number of universities implementing the educational program 30.05.03 — "Medical Cybernetics", and became the reason for the need to assess the situation with the training of students in this specialty.

   The aim of this work is to analyze the structure and subject content of the educational program 30.05.03 — "Medical Cybernetics" in universities of the Russian Federation.

   Materials and methods. Information presented on the official websites of universities and aggregator websites for applicants; documents regulating the educational process for specialty 30.05.03 "Medical Cybernetics".

   Results. The analysis of the main aspects of educational programs for specialty 30.05.03 "Medical Cybernetics" in ten universities of the Russian Federation showed their compliance with the general requirements of the Federal State Educational Standard for this specialty, with significant differences in approaches to subject content.

   Conclusion: Compliance of educational programs only with the formal requirements of the Federal State Educational Standard concerning their overall volume, structure and number of professional competencies does not guarantee that graduates meet the requirements of the professional standard "Cyberneticist". Clear criteria for acceptable differences in the subject content of the educational program are needed.

32-53 58
Abstract

   The requirements to anonymized real-world data (RWD), basic methods of anonymization and synthetization of RWD that allow to preserve their clinical informativeness are considered. The description of the procedure of collection, anonymization and use of RWD is given, which provides high stability of anonymized data in relation to threats of breach of confidentiality of information constituting medical confidentiality.

54-69 133
Abstract

   This article presents the design of a database intended to optimize the storage and processing of medical data, with a focus on decision support in intensive care and resuscitation.

   The aim of the study is to develop a logical database model based on advanced principles and methods used in international open database projects, capable of minimizing human error and enhancing the accuracy of real-time patient prognosis.

   The methodology is founded on a comparative analysis of existing international medical databases, such as MIMIC-IV and eICU. An innovative modular approach was applied in designing the new database, ensuring system flexibility and scalability. The primary outcome is the creation of a logical database model that can be effectively utilized within the Russian healthcare system, including remote and low-resource regions. The logical model was developed taking into account the specifics of medical data, including modules for storing information on hospitalizations, patient condition indicators, laboratory tests, medication prescriptions and other aspects of clinical practice. An important part of the study is the integration of the database with Russian medical information systems and adaptation to national standards and regulatory requirements. The developed architecture of the logical model minimizes the influence of the human factor, automates data analysis and can be used in the development of medical decision support systems. The practical significance lies in improving the quality of medical care and reducing the burden on the staff. The system is applicable in Russian institutions, including remote regions, and contributes to the digitalization of healthcare.

70-83 56
Abstract

   The paper presents an approach to design an information system based on a neural network graph architecture. This approach is designed to mitigate the problem of explicit explanation of decisions made by artificial intelligence — the problem of transparency (explainability, reliability, trustworthiness). The use of artificial intelligence technologies in medicine has a “transversal” character and contributes to the creation of conditions for improving efficiency and formation of fundamentally new areas of activity: automation of routine (repetitive) operations; use of autonomous intelligent equipment and robotic complexes, intelligent control systems; increasing the efficiency of planning, forecasting and medical decision-making processes. A promising technology of the proposed approach is the use of graph neural network architecture as part of the information system for data processing and analysis. In this article we introduce an example of graph node classification on an open dataset with cardio-data of conditionally healthy people and patients.

84-97 75
Abstract

   The study was set to assess the correspondence between neurophysiological and subjective indicators of motor imagination in the context of neurorehabilitation using brain-computer interfaces (BCIs). It was conducted as part of the development of a software and hardware complex (SHC) aimed at restoring cognitive and motor functions of the upper limbs in individuals with mild to severe impairments.

   Materials and Methods: Twenty-four healthy volunteers participated in the study. Electroencephalographic activity was recorded during motor imagery tasks involving different types of visual stimuli. The analysis included the calculation of sensorimotor desynchronization (ERD), classification using spatial filters and linear discriminant analysis, and correlation with subjective self-assessments.

   Results: The lateralization of imagined motion had a significant effect on ERD expression. Participants’ subjective confidence did not correlate with either neurophysiological measures or the classifier’s confidence in recognizing the imagined motion. However, the models demonstrated high accuracy in classifying motor representations.

   Conclusions: The identified discrepancy between subjective and objective assessment highlights the need to implement biofeedback and personalized BCIs into SHC systems to enhance the effectiveness of neurorehabilitation.

PRACTICE EXPERIENCE

98-106 57
Abstract

   The article discusses the process of development and approval of the first Code of Ethics of Artificial Intelligence (AI) application in the Russian Federation Healthcare. Against the backdrop of the active integration of AI technologies into medical practice (39 relevant medical devices have been registered), the emphasis is placed on the importance of establishing ethical standards that ensure the protection of patients' rights, increasing trust in technologies, and standardization processes. International approaches to AI ethics in healthcare (EU, USA, UK, Canada, Australia, China, India) are analyzed and the need to harmonize the domestic code with international initiatives is outlined. The stages of development of the document, in which employees of specialized departments of the Ministry of Health of Russia, chief freelance specialists and experts took part, as well as the structure and main provisions of the approved version of the Code are presented. The key principles emphasized include transparency, confidentiality, fairness, limited autonomy, oversight, and accountability of AI systems. The final version of the document was published in March 2025 on the Unified State Information System in Healthcare (EGISZ) portal after approval by the Interdepartmental Working Group under the Russian Ministry of Health. The Code is intended to serve as a foundation for the sustainable and safe implementation of AI in Russia's healthcare system.

ANNIVERSARIES



Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.