Artificial intelligence in medical technologies: models, methods, and prospects for clinical application

 Aim. To analyze artificial intelligence (AI) models and methods used in medical technologies and assess the prospects for their implementation in clinical practice, taking into account organizational, economic, ethical, and regulatory factors.

Material and methods. An analytical review of the research literature and experience of implementing AI technologies in healthcare was conducted. The review included domestic and international publications indexed in Scopus and PubMed for the period 2016-2026, as well as regulatory documents of the Russian Federation governing the development and use of AI-based medical devices. The focus was on publications containing data on the clinical validation of machine learning algorithms and deep neural networks.

Results. AI technоologies are actively used for medical image analysis, clinical data processing, and decisionmaking support. The most common methods are machine learning algorithms, deep neural networks, including convolutional architectures, and natural language processing methods. International studies demonstrate the high diagnostic accuracy of such algorithms, comparable to expert assessments by specialists. In the Russian Federation, AI technologies are being implemented primarily in radiology and medical image analysis. However, the effectiveness of their application largely depends on the quality of health data, clinical practice, the level of digital infrastructure, and regulatory frameworks.

Conclusion. AI has significant potential to improve the efficiency of the healthcare system, enhance diagnostic accuracy, and optimize clinical processes. However, its widespread implementation requires multicenter clinical trials, the development of national medical datasets, improved regulatory frameworks, and the training of medical specialists with competencies in data analysis and digital technologies. 

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