Aleksandrov A.Yu., Diomidova V.N., Ivanova N.N., Kopysheva T.N., Petrov K.A., Petrov A.R.

Public law regulation of the development, registration and implementation of medical decision support systems based on artificial intelligence

Keywords: public law regulation, medical decisions support systems, artificial intelligence in medicine, state registration of medical devices, state control (supervision), experimental legal regimes, adaptive regulation, information security

The development of medical decisions support systems based on artificial intelligence is significantly ahead of bringing the regulatory framework in line with technological realities. There are systemic legal conflicts in the field of qualification of such systems as medical devices, state control, information security and delineation of legal responsibility, which creates risks for patient safety and hinders technologies scaling. This determines the relevance of a comprehensive analysis of the problems of legal regulation. The purpose of the study is to identify and systematize regulatory problems that arise when developing, registering and implementing medical decisions support systems using artificial intelligence, as well as to substantiate areas for improving legal regulation in this area. Materials and methods. The research is based on the analysis of regulatory legal acts, national standards and scientific publications. Formal-logical, system-structural, comparative-legal methods and the method of legal modeling are applied. Results. A fragmented nature of legal regulation on the use of artificial intelligence in healthcare was revealed and the interrelation of regulatory problems in the field of state registration of medical devices, ensuring information security, delineating legal responsibility and financing digital healthcare was established. The article justifies the need to pass to adaptive regulation, which takes into account the dual nature of medical decisions support systems combining deterministic and probabilistic components. The following areas of improvement are proposed: differentiated registration separating the expertise of the artificial intelligence model and the knowledge base; imperative auditability and standardized explainability of algorithms; consolidation of the procedural status of audit logs; integration of national standards into administrative regulations; mechanisms of budget subsidy of technology implementation. Conclusions. The regulatory issues identified in the legal framework governing AI-based clinical decision support systems do not constitute a collection of isolated gaps, but rather form an interconnected system of contradictions arising from a fundamental mismatch between the ‘static’ architecture of current administrative legislation (which is geared towards products with fixed versions) and the ‘dynamic’ nature of modern cyber-physical systems with continuously updated knowledge bases. Targeted doctrinal adjustments to individual provisions will not be sufficient to resolve the systemic crisis of legal certainty. There is a need to move towards an adaptive regulatory model which, firstly, recognises the dual ontological nature of such systems and, secondly, ensures flexibility without compromising the public interest in the field of health protection. The proposed mechanism – differentiated registration, mandatory auditability, standardised explainability and financial support – is not a set of disparate measures, but a unified regulatory mechanism that strikes a balance between fostering innovation and safeguarding the public interest in the field of public health. The provisions set out here are of both theoretical and practical significance: they can be used in drafting subordinate legislation by the Russian Ministry of Health and Roszdravnadzor, as well as in forming a consistent approach to law enforcement in the field of the distribution of medical devices based on artificial intelligence.

About authors

Aleksandrov Andrey Yu.

Candidate of Economics Sciences, Associate Professor, Rector, Chuvash State University, Russia, Cheboksary (rector@chuvsu.ru; ORCID: https://orcid.org/0000-0003-3489-9029)

Diomidova Valentina N.

Doctor of Medical Sciences, Professor, Head of the Department of Propaedeutics of Internal Diseases with a Course of Radiation Diagnostics, Chuvash State University; Head of the Department of Ultrasound Diagnostics, City Clinical Hospital № 1, Russia, Cheboksary (diomidovavn@rambler.ru; ORCID: https://orcid.org/0000-0002-3627-7971)

Ivanova Nadezhda N.

Candidate of Technical Sciences, Associate Professor, Department of Mathematical and Hardware Support of Information Systems, Chuvash State University, Russia, Cheboksary (niva_mail@mail.ru; ORCID: https://orcid.org/0000-0001-7130-8588)

Kopysheva Tatyana N.

Candidate of Physical and Mathematical Sciences, Associate Professor, Head of the Department of Mathematical and Hardware Support of Information Systems, Chuvash State University, Russia, Cheboksary (tn_pavlova@mail.ru; ORCID: https://orcid.org/0000-0003-3392-1431)

Petrov Kirill A.

Post-Graduate Student, Senior Lecturer, Department of Mathematical and Hardware Support of Information Systems, Chuvash State University, Russia, Cheboksary (kirillapetrov2000@yandex.ru; ORCID: https://orcid.org/0009-0004-0989-0078)

Petrov Aleksandr R.

Post-Graduate Student, Assistant Lecturer, Department of Mathematical and Hardware Support of Information Systems, Chuvash State University; Head of the Training Center, Keysystems LLC, Russia, Cheboksary (petrick@keysystems.ru; )

Article link

Aleksandrov A.Yu., Diomidova V.N., Ivanova N.N., Kopysheva T.N., Petrov K.A., Petrov A.R. Public law regulation of the development, registration and implementation of medical decision support systems based on artificial intelligence [Electronic resource] // Oeconomia et Jus. – 2026. – №3. P. 51-61. – URL: https://oecomia-et-jus.ru/en/single/2026/3/4/. DOI: 10.47026/2499-9636-2026-3-51-61.

Copyright holder: authors. License: Attribution 4.0 International (CC BY 4.0)