Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Digital solutions in anesthesiology and intensive care: an analysis of trends in Russian scientific discourse
0
Zitationen
1
Autoren
2026
Jahr
Abstract
Introduction. This article presents an analytical review of scientific publications by Russian authors over the past ten-year period from 2015 to 2024, reflecting the processes of digital transformation of anesthesiology and intensive care services in Russian medical institutions. The objective was to systematize and critically comprehend the main directions, outcomes and limitations of domestic research in the field of digitalization of the anesthesiology and intensive care services and to identify key trends, knowledge gaps and promising vectors of further development. Methods and materials. The main keywords and their combinations were used to search for publications in the Russian electronic scientific library eLibrary for the period from 2015 to 2024. Results. The analysis of publications is intended not only to describe the current state and dynamics of digital technology development in anesthesiology and intensive care in Russia, but also to highlight the most significant scientific and practical foundations, as well as potential bottlenecks and barriers to scaling up successful solutions. Conclusions. The absence of a comprehensive synthesis of Russian research on the digitalization of anesthesiology and intensive care services limits the ability to develop coordinated development strategies, set priorities for science and technology policy, and inform governmental decision‑making in the field of medical informatics and artificial intelligence technologies in anesthesiology and critical care medicine.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.635 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.543 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.051 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.844 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.