Author
Listed:
- N. N. Lisitskii
- T. G. Maximova
Abstract
The aim of the study was to identify the key characteristics of digital healthcare innovations to determine optimal strategic approaches for their development. A text corpus of regional digital healthcare practices, selected by the Ministry of Health of the Russian Federation and the Central Research Institute of Organization and Informatization of Healthcare (FSBI “RIOIH†) of the Ministry of Health of Russia, was compiled as the empirical basis for the analysis. Using the statistical TF-IDF method to identify semantically significant terms, key patterns characterizing digital healthcare solutions are identified. The predominant role of innovations aimed at organizing primary healthcare is revealed. The most characteristic artificial intelligence technologies in digital healthcare innovations are robotic voice assistants and computer vision technologies. Imperfections in regulatory frameworks concerning the application of medical technologies based on artificial intelligence, the existing digital infrastructure, and issues of ethics and safety in the use of medical data hinder the widespread adoption of innovations. In this regard, strategic approaches to the implementation of artificial intelligence in the context of digital transformation are being defined. The relevance of developing and implementing non-medical digital innovations based on artificial intelligence into the routine processes of medical organizations is demonstrated. The advisability of the widespread use of artificial intelligence in routine innovations to create a holistic data ecosystem necessary for forecasting and strategic decision-making is explained.Â
Suggested Citation
N. N. Lisitskii & T. G. Maximova, 2025.
"Exploring Artificial Intelligence Patterns in Digital Innovation: Insights from the Healthcare Sector,"
Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 5.
Handle:
RePEc:acf:journl:y:2025:id:2838
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