Code of ethics for the use of artificial intelligence in the Russian Federation healthcare
Bibliographic record
Abstract
The article discusses the process of development and approval of the first Code of Ethics of Artificial Intelligence (AI) application in the Russian Federation Healthcare. Against the backdrop of the active integration of AI technologies into medical practice (39 relevant medical devices have been registered), the emphasis is placed on the importance of establishing ethical standards that ensure the protection of patients' rights, increasing trust in technologies, and standardization processes. International approaches to AI ethics in healthcare (EU, USA, UK, Canada, Australia, China, India) are analyzed and the need to harmonize the domestic code with international initiatives is outlined. The stages of development of the document, in which employees of specialized departments of the Ministry of Health of Russia, chief freelance specialists and experts took part, as well as the structure and main provisions of the approved version of the Code are presented. The key principles emphasized include transparency, confidentiality, fairness, limited autonomy, oversight, and accountability of AI systems. The final version of the document was published in March 2025 on the Unified State Information System in Healthcare (EGISZ) portal after approval by the Interdepartmental Working Group under the Russian Ministry of Health. The Code is intended to serve as a foundation for the sustainable and safe implementation of AI in Russia's healthcare system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".