Standardization in regulating artificial intelligence systems in Russian healthcare

Cover Page


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

Artificial intelligence technologies in medical practice are a promising direction in the world. Artificial intelligence medical decision support systems, diagnostic and screening programs can help medical personnel in routine and complex tasks and improve the level of medical care provided to patients. At the same time, the development, production and distribution of artificial intelligence systems must be regulated without fail. Registration and subsequent control (post-registration monitoring) of artificial intelligence systems in medicine require the creation, adjustment of the legal framework and technological regulation. The Russian Federation has developed a promising development strategy in this area. Seven national standards have been developed by experts in the field of Artificial intelligence in healthcare. These standards establish the procedures for conducting clinical and technical trials, performance requirements and the concept of life cycle, a quality management system and risk management. A separate standards is devoted to dataset creation for training and testing the developed algorithms, requirements for them and a metadata format. There are plans to bring the developed national standards to the international level, which will allow Russian manufacturers of artificial intelligence systems implemented these national standards to comply with foreign counterparts and become more competitive at the international level. The international community has already supported the development of an ISO standard based on the national standard for clinical trials. The development will be performed based on the technical committee ISO/TC 215 (Health informatics) in conjunction with ISO/IEC JTC 1/SC 42 (Artificial intelligence), this will allow bringing the national requirements for the Artificial intelligence to the international level. The cycle of these standards will summarize recognized methodologies, helping both manufacturers and medical organizations, doctors and patients to produce and use a quality, safe and effective product.

Full Text

Restricted Access

About the authors

V. V. Zinchenko

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: v.zinchenko@npcmr.ru
ORCID iD: 0000-0002-2307-725X
SPIN-code: 4188-0635
Scopus Author ID: 57191350361
Russian Federation, Moscow, Russia

A N Khoruzhaya

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: a.khoruzhaya@npcmr.ru
ORCID iD: 0000-0003-4857-5404
SPIN-code: 7948-6427
ResearcherId: AAG-5184-2020
Russian Federation, Moscow, Russia

D E. Sharova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: d.sharova@npcmr.ru
ORCID iD: 0000-0001-5792-3912
SPIN-code: 1811-7595
Russian Federation, Moscow, Russia

E S Akhmad

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Author for correspondence.
Email: e.ahmad@npcmr.ru
ORCID iD: 0000-0002-8235-9361
Russian Federation, Moscow, Russia

O A Mokienko

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: o.mokienko@npcmr.ru
ORCID iD: 0000-0002-7826-5135
SPIN-code: 8088-9921
Scopus Author ID: 55155448000
ResearcherId: J-3210-2016
Russian Federation, Moscow, Russia

A V Vladzymyrskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: a.vladzimirsky@npcmr.ru
ORCID iD: 0000-0002-2990-7736
SPIN-code: 3602-7120
Scopus Author ID: 8944262100
ResearcherId: D-1447-2017
Russian Federation, Moscow, Russia

S P Morozov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: morozov@npcmr.ru
ORCID iD: 0000-0001-6545-6170
SPIN-code: 8542-1720
Scopus Author ID: 57200964938
ResearcherId: T-9163-2017
Russian Federation, Moscow, Russia

References

  1. Meldo A.A., Utkin L.V., Moiseyenko V.M. XXI century diagnostic algorithms. Artifitial intelligance in lung cancer detection. Prakticheskaya onkologiya. 2018; 19 (3): 292–298. (In Russ.) doi: 10.31917/1903292.
  2. Borodulina E.A. Artificial intelligence in tuberculosis detection. Opportunities and prospects. The Doctor. 2020; 31 (5): 30–33. (In Russ.) doi: 10.29296/25877305-2020-05-06.
  3. Castaldi P.J., Boueiz A., Yun J., Estepar R.S.J., Ross J.C., Washko G., Cho M.H., Hersh C.P., Kinney G.L., Young K.A., Regan E.A., Lynch D.A., Criner G.J., Dy J.G., Rennard S.I., Casaburi R., Make B.J., Crapo J., Silverman E.K., Hokanson J.E.; COPDGene Investigators. Machine Learning characterization of COPD Subtypes: insights from the COPDGene Study. Chest. 2020; 157.5: 1147–1157. doi: 10.1016/j.chest.2019.11.039.
  4. Retson T.A., Eghtedari M. Computer-Aided detection/diagnosis in breast imaging: a focus on the evolving FDA regulations for using software as a medical device. Curr. Radiol. Rep. 2020; 8: 7. doi: 10.1007/s40134-020-00350-6.
  5. Gusev A.V., Gavrilov D.V., Korsakov I.N., Serova L.M., Novitsky R.E., Kuznetsova T.Yu. Prospects for the use of machine learning methods for predicting cardiovascular disease. Vrach i informatsionnye tekhnologii. 2019; (3): 41–47. (In Russ.)
  6. He J., Baxter S.L., Xu J., Xu J., Zhou X., Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019; 25: 30–36. doi: 10.1038/s41591-018-0307-0.
  7. Ranschaert E.R., Morozov S.P., Algra P.R. Artificial Intelligence in Medical Imaging. 1st ed. Springer Internatio­nal Publishing. 2019; 373 p. doi: 10.1007/978-3-319-94878-2.
  8. Gusev A.V., Dobridnyuk S.L. Artificial intelligence in medicine and healthcare. Informatsionnoe obshchestvo. 2017; (4–5): 78–93. (In Russ.)
  9. Kurakova N.G., Tsvetkova L.A., Cherchenko O.V. Artificial intelligence techno­logies in medicine and healthcare: Russia's position on the global patent and publication landscape. Vrach i informa­tsionnye tekhnologii. 2020; (2): 81–100. (In Russ.) doi: 10.37690/1811-0193-2020-2-81-100.
  10. Meldo A.A., Utkin L.V., Trofimova T.N. Artificial intelligence in medicine: current state and main directions of development of the intellectual diagnostics. Luchevaya diagnostika i terapiya. 2020; (1): 9–17. (In Russ.) doi: 10.22328/2079-5343-2020-11-1-9-17.
  11. Le­bedev G.S., Fomina I.V., Shaderkin I.A., Lisnenko A.A., Ryabkov I.V., Kachkovskiy S.V., Melaev D.V. Main directions for development of internet-technologies in health care (systematic review). Social aspects of population health. 2017; 57 (5): 10. (In Russ.) doi: 10.21045/2071-5021-2017-57-5-10.
  12. Karpov O.E., Klimenko G.S., Lebеdev G.S. Application of intelligent systems in health care. Modern high technologies. 2016; (7-1): 38–43. (In Russ.)
  13. Gusev A.V., Zarubina T.V. Clinical Decisions Support in medical information systems of a medical organization. Vrach i informatsionnye tekhnologii. 2017; (2): 60–72. (In Russ.)
  14. Elenko E., Speier A., Zohar D. A regulatory framework emerges for digital medicine. Nat. Biotechnol. 2015; 33: 697–702. doi: 10.1038/nbt.3284.
  15. Hwang T.J., Kesselheim A.S., Vokinger K.N. Lifecycle regulation of artificial intelligence — and machine learning-based software devices in medicine. JAMA. 2019; 322 (23): 2285–2286. doi: 10.1001/jama.2019.16842.
  16. Goodman K.W. Ethics in health informatics. Yearbook of medical informatics. 2020; 29 (1): 26–31. doi: 10.1055/s-0040-1701966.
  17. Andreeva I.L., Natenzon M.Y. Priorities for the development of innovative digital healthcare in Russia. Health care standardization problems. 2017; (11–12): 3–9. (In Russ.) doi: 10.26347/1607-2502201711-12003-009.
  18. IMDRF/SaMD WG/N41FINAL: 2017. Software as a Medical Device (SaMD): Clinical Evaluation. http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-170921-samd-n41-clinical-evaluation_1.pdf (­access date: 02.09.2021).
  19. Regulatory Guidelines for Software Medical Devi­ces — A Life Cycle Approach. April 2020. Singapore. https://www.hsa.gov.sg/docs/default-source/announcements/regulatory-updates/regulatory-guidelines-for-software-medical-devices--a-lifecycle-approach.pdf (access date: 02.09.2021).
  20. FDA. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) — Discussion Paper and Request for Feedback. https://www.kslaw.com/attachments/000/007/073/original/7-1-19_Intellectual_Property___Technology_Law_Journal.pdf?1562866795 (access date: 02.09.2021).
  21. The National Artificial Intelligence Research and Development Strategy Plan (2016–2019). https://www.nitrd.gov/pubs/National-AI-RD-Strategy-2019.pdf (access date: 20.09.2021).
  22. Yaeger K.A., Martini M., Yaniv G., Oermann E.K., Costa A.B. United States regulatory approval of medical devices and software applications enhanced by artificial intelligence. Health Policy and Technology. 2019; 8 (2): 192–197. doi: 10.1016/j.hlpt.2019.05.006.
  23. AI in Korea. https://www.oecd.ai/dashboards/countries/SouthKorea (access date: 20.09.2021).
  24. On artificial intelligence — A European approach to excellence and trust. https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en (access date: 20.09.2021).
  25. Schneeberger D., Stöger K., Holzinger A. The European Legal Framework for Medical AI. In: Lecture Notes in Computer Science. Vol. 12 279, Machine learning and knowledge extraction. 2020; 209–226. doi: 10.1007/978-3-030-57321-8.
  26. In their own words — New Generation Artificial Intelligence Development Plan. https://www.airuniversity.af.edu/CASI/Display/Article/2521258/in-their-own-words-new-generation-artificial-intelligence-development-plan/ (access date: 20.09.2021).
  27. Reddy S., Allan S., Coghlan S., Cooper P. A governance model for the application of AI in health care. J. Am. Med. Inform. Assoc. 2020; 27 (3): 491–497. doi: 10.1093/jamia/ocz192.
  28. Passport of the national program “Digital Economy of the Russian Fe­deration” (approved by the Presidium of the Council under the President of the Russian Federation for Strategic Development and National Projects, minutes of December 24, 2018 No. 16). http://government.ru/info/35568/ (access date: 03.09.2021). DOI: http://dx.doi.org/10.2471/BLT.13.020813.
  29. The Order of the Federal Agency for Technical Regulation and Metrology №1732, issued at 25.07.2019 “On the creation of a technical committee for standardization “Artificial Intelligence””. http://www.consultant.ru/cons/cgi/online.cgi?req=doc&base=EXP&n=735452#4SISMoSvOiyks9EN (access date: 03.09.2021). (In Russ.)
  30. Order of the Technical Committee for Standardization ‘Artificial Intelligence” No. 1, issued at January 13, 2020 “On the definition of the basic organization of the subcommittee “Artificial Intelligence in Healthcare””. https://tele-med.ai/media/uploads/2021/03/18/01_-2-2.pdf (access date: 03.09.2021). (In Russ.)
  31. Decision of the Council of the Eurasian Economic Commission No. 29, issued at February 12, 2016 “On the rules for conducting clinical and cli­nical laboratory tests (research) of medical devices”. https://docs.eaeunion.org/docs/ru-ru/01410222/cncd_17052016_29 (access date: 20.09.2021). (In Russ.)
  32. Decision of the Council of the Eurasian Economic Commission No. 28, issued at 12.02.2016 “On approval of the Rules for conducting technical tests of medical devices”. https://docs.eaeunion.org/docs/ru-ru/01410219/cncd_17052016_28 (access date: 20.09.2021). (In Russ.)
  33. Belousov D.Yu., Zyryanov S.K., Kolbin A.S. Upravlenie klinicheskimi issledovaniyami. (Clinical research management.) 1st ed. M.: Buki Vedi, Izdatel'stvo OKI. 2017; 676 p. (In Russ.)
  34. Pesapane F., Volonté C., Codari M., Sardanelli F. Artificial intelligence as a medical device in radiology: ­ethical and regulatory issues in Europe and the Uni­ted States. Insights Imaging. 2018; 9: 745–753. doi: 10.1007/s13244-018-0645-y.
  35. Marc D.K., Ronald M.S., Raymond G. Medical ­image data and datasets in the era of machine learning — whitepaper from the 2016 C-MIMI Meeting Dataset Session. J. Digit. Imaging. 2017; 30: 392–399. doi: 10.1007/s10278-017-9976-3.
  36. Gerke S., Minssen T., Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. In: Artificial Intelligence in Healthcare. Academic Press. 2020; 295–336. doi: 10.1016/B978-0-12-818438-7.00012-5.
  37. Kupriyanovsky V.P., Yartsev D.I., Utkin N.A., Namiot D.E. Eco­nomy standards in the digital age and information and communication technologies on the example of the British Standards Institute. Intern. J. Open Inform. Technol. 2016; 4 (6): 1–9. (In Russ.)
  38. Gusev A.V., Pliss M.A. The basic recommendations for the creation and development of information systems in health care based on artificial intelligence. Vrach i informatsionnye tekhnologii. 2018; (3): 45–60. (In Russ.)

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Рис. 1. Семь разрабатываемых стандартов в области регулирования систем искусственного интеллекта (СИИ); ГОСТ — государственный стандарт [Научно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения г. Москвы]

Download (43KB)
Indexing metadata
3. Рис. 2. Этапы клинической оценки систем искусственного интеллекта (СИИ); ПО — программное обеспечение; НПА — нормативно-правовые акты [Научно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения г. Москвы]

Download (78KB)
Indexing metadata
4. Рис. 3. Процесс подготовки набора данных для обучения и тестирования систем искусственного интеллекта [Научно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения г. Москвы]

Download (28KB)
Indexing metadata
5. Рис. 4. Процессы жизненного цикла систем искусственного интеллекта; ИИ — искусственный интеллект [Научно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения г. Москвы]

Download (39KB)
Indexing metadata
6. Рис. 5. Перспективная программа стандартизации в области медицинских изделий на период 2020–2027 гг.; ГОСТ — государственный стандарт; ЖЦ — жизненный цикл; СМК — система менеджмента качества; СИИ — система искусственного интеллекта; СППВР — система поддержки принятия врачебных решений [Научно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения г. Москвы]

Download (99KB)
Indexing metadata

© 2021 Eco-Vector




This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies