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).