Trends and Directions of Artificial Intelligence Technology Application in Medicine: An Analytical Review
Rubrics: SCIENTIFIC ARTICLES
Authors:
1.
Tver State Technical University
(Associate Professor)
Russian Federation
Russian Federation
2.
Tver State Technical University
student
student
Type:
Article
Pages:
from 230
to 233
Status:
Published
Received:
08.02.2026
Accepted:
12.02.2026
Published:
12.02.2026
Subject area:
UDC 004.9
Language:
Russian
Keywords:
medicine, artificial intelligence, ethics, trust, healthcare
Abstract:
The article analyzes the key trends and directions of implementing artificial intelligence (AI) technologies in healthcare. The main focus is on the systemic crisis of trust in AI, caused by the mismatch between the dynamic, probabilistic nature of AI systems and traditional static regulatory approaches. As a solution, the authors, drawing on an international expert report, propose an evolutionary transformation of the governance model based on three interrelated principles. First, it is the transition to adaptive regulation through "soft law" (guidelines and standards) that can be promptly updated. Second, the creation of regulatory "sandboxes" — controlled environments for testing AI solutions in real-world conditions with temporary relaxation of rules. Third, the implementation of continuous post-market surveillance systems for AI algorithms throughout their entire lifecycle. The necessity of moving from a "regulator vs. developer" confrontation to a public-private partnership model is emphasized, where business acts as a co-developer of standards and a provider of infrastructure. The conclusion highlights the importance of increasing digital literacy among medical professionals and regulators, as well as the need for international harmonization of approaches to overcome the global "digital divide" in access to advanced AI medical technologies.
The article analyzes the key trends and directions of implementing artificial intelligence (AI) technologies in healthcare. The main focus is on the systemic crisis of trust in AI, caused by the mismatch between the dynamic, probabilistic nature of AI systems and traditional static regulatory approaches. As a solution, the authors, drawing on an international expert report, propose an evolutionary transformation of the governance model based on three interrelated principles. First, it is the transition to adaptive regulation through "soft law" (guidelines and standards) that can be promptly updated. Second, the creation of regulatory "sandboxes" — controlled environments for testing AI solutions in real-world conditions with temporary relaxation of rules. Third, the implementation of continuous post-market surveillance systems for AI algorithms throughout their entire lifecycle. The necessity of moving from a "regulator vs. developer" confrontation to a public-private partnership model is emphasized, where business acts as a co-developer of standards and a provider of infrastructure. The conclusion highlights the importance of increasing digital literacy among medical professionals and regulators, as well as the need for international harmonization of approaches to overcome the global "digital divide" in access to advanced AI medical technologies.
Keywords:
medicine, artificial intelligence, ethics, trust, healthcare
medicine, artificial intelligence, ethics, trust, healthcare
1. Earning Trust for AI in Health: A Collaborative Path Forward – Rezhim dostupa: https://www.weforum.org/publications/earning-trust-for-ai-in-health-a-collaborative-path-forward/ (data obrascheniya 01.12.2025)
2. Iskusstvennyy intellekt: tehnologii obucheniya, tipy i sfery primeneniya – Rezhim dostupa: https://serverflow.ru/blog/stati/iskusstvennyy-intellekt-tekhnologii-obucheniya-tipy-i-sfery-primeneniya/ (data obrascheniya 03.12.2025)
3. AI Airlock: the regulatory sandbox for AIaMD – Rezhim dostupa: https://www.gov.uk/government/collections/ai-airlock-the-regulatory-sandbox-for-aiamd (data obrascheniya 28.11.2025)
