Articles

Network-centric public governance offers clear advantages, yet its effective adoption requires overcoming a set of constraints and charting practical implementation pathways. Drawing on a comparative analysis of centralized and decentralized governance systems, the article details the benefits of a network-centric approach, including enhanced data generation, storage, distribution and exchange; greater system resilience; improved discovery and use of data; and stronger assurance of data veracity and trust in data sources. It explicates core principles of network-centric governance—decentralization, openness, interactivity, flexibility, technological integration, collaboration, and security—and identifies key implementation challenges such as cyber threats, the digital divide, technological dependency, diffusion (decentralization) of authority, technological risks, and legal risks. The study also outlines the ontological foundations of network-centric public governance, provides a comparative assessment of hierarchical, network, and network-centric governance models, and substantiates the conceptual specificities of network-centric state management. The conclusion argues for the development of hybrid managerial systems that integrate hierarchical and network-centric logics of coordination. Implementing a network-centric approach opens promising avenues for optimizing public governance.

The article discusses a new approach in healthcare related to the identification of digital determinants of health, which has recently become widespread in the analysis of healthcare problems. Consideration of the main conceptual provisions showed that, despite their importance for understanding health problems, further research of digital determinants of health is required, in particular, it is necessary to determine their relationship with other determinants of health, first of all social and economic.

Based on Rosstat data, a general description of the state of selected digital determinants in Russia is given, in particular, Internet accessibility, the main areas of its use in terms of health protection, the state of digitalization of medical services are considered. It is concluded that despite the fact that digitalization is an important priority for the development of modern healthcare in Russia, further research is needed on its impact not just on individual parameters of health care system, but also on the impact on the final result — improving population health. Th e approach of digital determinants of health seems to provide a solid theoretical and practical basis for solving this problem.

Artificial Intelligence (AI) is transforming the pharmaceutical lifecycle —from discovery to manufacturing and safety oversight. In discovery, AI accelerates target identification, molecular design, and repurposing (e.g., baricitinib; rentosertib), while the DSP-1181 experience highlights the risk of structural conservatism. In clinical development, AI supports patient stratification, digital-twin-informed design, and automated analysis, improving statistical power and operational speed. In manufacturing, predictive analytics, computer vision, and knowledge-graph-assisted documentation enhance quality and throughput. Pharmacovigilance benefits from natural language processing (NLP) — enabled signal detection and workflow automation. Emerging uses in antibody engineering and personalized therapy broaden its scope, but real-world benefit hinges on data quality, rigorous validation, transparency, and regulatory alignment (including real-world evidence).

The widespread introduction of artificial intelligence(AI) capabilities into the healthcare system creates new requirements and conditions for the training of medical personnel. Th e article is devoted to the study of modern challenges, aspects of methodology and the prospect of training specialists for successful work with AI technologies. Th is study contains an analysis of the most relevant areas for the application of AI systems, an analysis of the requirements for the competencies of specialists, innovations in the educational process and ethical aspects. Special attention is paid to building trust in new technologies, overcoming long-standing organizational barriers and creating modern training programs. Th e results of the study show the importance of a systematic approach to personnel training, taking into account technical education, critical thinking and ethical and psychological training.

Artificial intelligence plays an important role in healthcare in general and in pharmaceuticals in particular. It is used to improve drug development processes, disease diagnostics, and supply chain management. In pharmaceutical practice, artificial intelligence can significantly improve drug administration and patient care. At the same time, there are also certain problems with the active penetration of AI into almost all areas of healthcare, and it is difficult to determine whether they are temporary or long-term. From a management perspective, the problem of interaction between key actors (stakeholders) influencing both policy formation in this area and the possibilities and limitations of AI application in healthcare is relevant.

Clinical decision support systems (CDSS) are actively being implemented in the digital support of medical organizations, providing doctors with prompt ac cess to scientifi c and methodological evidence-based knowledge and medical data at all stages of medical care. The experience of using CDSS has shown improved patient treatment outcomes, reduced the number of medical errors, and increased clinical and cost-effectiveness in general. The study of the potential capabilities of the CDSS continues and the prospects for future developments in this area are being determined. At the same time, there are a number of problems related to the implementation of the CDSS, of which the problems of medical data security, system integration and approval of the soft ware product by medical specialists remain the most priority. CDSS have demonstrated significant potential, but their implementation and optimization remain challenging. Th e article provides an overview of research publications on the development and implementation of CDSS in medicine, the role of artificial intelligence (AI) and machine learning (ML) in the development of new models. Decision support systems for medical professionals and patients are among the priorities of applied scientific research in the interests of medicine and healthcare proposed by the Russian Ministry of Health. These types of developments are included in the section of innovative technologies in the field of digital management in the healthcare sector.

Artificial intelligence technologies have been intensively implemented in the healthcare sector in Russia since 2023. A priority area for the introduction of AI technologies is the use of medical devices with AI technologies (MD with AI) and their introduction into the contour of state information systems in the healthcare sector of the subjects of the Russian Federation. To analyze the outcomes of the development and deployment of AI technologies in the healthcare sector in the Russian Federation. As of July 7, 2025, Roszdravnadzor registered 47 medical devices with AI technologies, 29 AI solutions focused on radiological image analysis, 6 on electronic medical record data analysis, 2 AI solutions for video stream analysis, 2 AI solutions for digital ECG analysis, 2 AI solutions for surgery planning and 1 AI solution for pharmaceutical therapy analysis. More than 200 new innovative domestic AI solutions with diff erent levels of technological readiness are being created in subordinate organizations of the Russian Ministry of Health. Th ere are more than 190 data sets, as well as about 100 clinical tasks that can be solved using AI. In 2023, all subjects of the Russian Federation started implementing AI in thehealthcare sector (with the exception of new subjects of the Russian Federa tion). As of December 31, 2024, 412 Missions have been implemented. Since 2023, AI technologies have been intensively implemented in healthcare, regulatory conditions for the use of AI have been created, and innovative solutions with AI technologies have been created and implemented.