DOI: 10.25881/BPNMSC.2020.73.34.027


Karpov O.E., Penzin O.V., Veselova O.V.

Pirogov National Medical and Surgical Center, Moscow


The ubiquitous digital transformation of healthcare leads to the development and implementation of solutions using artificial intelligence technologies. This brings many benefits, but introduces new and specific problems in the relationship between doctors, patients, and regulators.

Approaches to their solution proposed by international bodies, consultants and practitioners are considered. It is concluded that the driver of the introduction of artificial intelligence in the industry is the presence of high-quality big medical data.

It is proposed to support the proposals of some Russian regulators on depersonalization, decentralization and deregulation of medical data accumulated by scientific and clinical centers to accelerate the creation of smart and ethical medical decision support systems.

Keywords: artificial intelligence, medicine, medical decision support systems, real-world data, RWD, medical data.


1. Willie MM, Nkomo P. Digital transformation in healthcare — South Africa context. World Medical Journal. 2020;66(1):34–38.

2. Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of health care. Am J Med. 2019;132(7):795–801. Doi: 10.1016/j.amjmed.2019.01.017.

3. Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. 2018;131(2):129–133. Doi: 10.1016/j.amjmed.2017.10.035.

4. Map of artificial intelligence of Russia v1.17. IP Laboratory, LLC. [cited 2020.07.28].

5. Preliminary study on the technical and legal aspects relating to the desirability of a standard-setting instrument on the ethics of artificial intelligence. UNESCO Executive Board Conference Proceedings. Paris; 2019. 34 p. [cited 2020.07.20].

6. Federal Law No. 123 “O provedenii eksperimenta po ustanovleniyu spetsial'nogo regulirovaniya v tselyakh sozdaniya neobkhodimykh uslovii dlya razrabotki i vnedreniya tekhnologii iskusstvennogo intellekta v sub"ekte Rossiiskoi Federatsii — gorode federal'nogo znacheniya Moskve i vnesenii izmenenii v stat'i 6 i 10 Federal'nogo zakona "O personal'nykh dannykh” dated April 24, 2020.

7. WMA statement on augmented intelligence in medical care. Adopted by the 70th World Medical Association General Assembly. Tbilisi, Georgia; 2019 [cited 2020 Jul 20].

8. Engelbart DC. Augmenting Human Intellect: a conceptual framework. Summary Report AFOSR-3233. Menlo Park: Stanford Research Institute; 1962.

9. Chuchalin AG, Chereshnev VA, Mishlanov VYu, et al. Bioethics, artificial intelligence and medical diagnostics. Perm; 2019. P. 174–175.

10. Information letter of Roszdravnadzor No. 02I-297/20 dated February 13, 2020.

11. Stolbov AP. About the classification of risks of application of the medical software in the Eurasian economic union. Information technologies for the Physician. 2019;(3):22–31.

12. Rules for the classification of medical devices depending on the potential risk of use. Decision of the Board of the Eurasian Economic Commission No. 173 dated December 22, 2015.

13. International Medical Device Regulators Forum. IMDRF/SaMD WG(PD1)/N41R3: 2016 Software as a Medical Device: Clinical Evaluation, 2017 [cited 2020 Jul 20].

14. Gartner survey of EHR suppliers and systems in the Norwegian market. Version 1.0. Gartner, Inc. Commissioned by the Ministry of Health and Care Services. 2014. p. 9–11 [cited 2020 Jul 20].

15. Geis JR, Brady A, Wu CC, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging. 2019;10(1):101. Doi: 10.1186/s13244-019-0785-8.

16. The Royal Australian and New Zealand College of Radiologists. RANZCR proposes ethical guidelines for AI. Draft [cited 2020 Jul 20].

17. Karpov OE, Subbotin SA, Shishkanov DV. Medical data usage to create medical decision support systems. Information technologies for the Physician. 2019;(2):11–18.

18. Walker J, Lovett R, Kukutai Е, et al. Indigenous health data and the path to healing. Lancet. 2017;390(10107):2022–2023. Doi: 10.1016/S0140-6736(17)32755-1.

19. You SC, Lee S, Cho SY, et al. Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). Stud Health Technol Inform. 2017;245:467–470.

20. Hripcsak G, Ryan PB, Duke JD, et al. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci U S A. 2016;113(27):7329–7336. Doi: 10.1073/pnas.1510502113

21. Draft Law “O vnesenii izmenenii v otdel'nye zakonodatel'nye akty Rossiiskoi Federatsii v svyazi s prinyatiem Federal'nogo zakona “Ob eksperimental'nykh pravovykh rezhimakh v sfere tsifrovykh innovatsii v Rossiiskoi Federatsii”. (In Russ).] [cited 2020 Jul 20].

For citation

Karpov O.E., Penzin O.V., Veselova O.V. Organization and regulation of artificial intelligence with a doctor and a patient interaction. Bulletin of Pirogov National Medical & Surgical Center. 2020;15(2):155-160. (In Russ.)