Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Implementation of artificial intelligence technologies in Russian healthcare: results of 2018–2024
0
Zitationen
4
Autoren
2025
Jahr
Abstract
Background: Healthcare is one of the priority sectors for the implementation of artificial intelligence (AI) worldwide, including Russia. The key area of AI implementation is the integration of AI-based medical devices into the Unified Digital Framework in the healthcare sector of the constituent entities of the Russian Federation. The aim of this study is to analyze the development and implementation outcomes of AI in the Russian healthcare system in 2018–2024. Materials and methods: The data regarding AI implementation were extracted from legislation, scientific publications and provided by the Ministry of Health of the Russian Federation and the national technical committee for standardization in the AI technologies. Results: In Russia, 77% of AI-based medical devices are intended for medical image analysis. Between 2018 and 2024, 69% of investments in the development and implementation of AI solutions in healthcare came from state sources. Scientific research in this field is actively progressing: research institutions under the Russian Ministry of Health are implementing 215 AI-related healthcare projects. A total of 21 national and pre-liminary technical standards in the field of AI for healthcare have been developed and approved. In 2023, the deployment of AI-based medical devices began across the Russian regions. As of 01.01.2025, a total of 412 AI-based medical devices had been implemented, of which 83% are used for image analysis and 16% for electronic health record analysis. Conclusion: A set of measures is being developed to actively introduce AI into healthcare, including the legal frameworks, attracting investments, conducting research and developing new products.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.339 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.211 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.614 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.478 Zit.