Evolution of research and development in the field of artificial intelligence technologies for healthcare in the Russian Federation: results of 2021

Capa

Citar

Texto integral

Resumo

The use of artificial intelligence technologies in Russian healthcare is a priority area for implementing a national strategy for the development of artificial intelligence in the country. The introduction of digital solutions based on artificial intelligence in healthcare facilities should improve the standard of living of the population and the quality of medical care, including areas of preventive examinations, diagnostics based on image analysis, prediction of disease development, selection of optimal drug dosages, reducing the threat of pandemics, and automating and increasing the accuracy of surgical interventions.

Policy management and technical regulation are under development in the field of artificial intelligence in healthcare. The domestic market for relevant solutions has been created, and some products have been certified as medical devices from Roszdravnadzor (Federal Service for Surveillance in Healthcare). Various teams of scientists are conducting research. However, Russia is still behind the leading countries in the field of artificial intelligence, such as the United States and China. Investments in healthcare products based on artificial intelligence decreased significantly in 2021. The major reasons for the lag, at least in terms of market indicators, are low demand and the inability of state medical organizations to fund artificial intelligence projects. There are also other issues related to trust in the safety and effectiveness of such solutions.

Sobre autores

Aleksander Gusev

K-Skai; Russian Research Institute of Health

Autor responsável pela correspondência
Email: agusev@webiomed.ai
ORCID ID: 0000-0002-7380-8460
Código SPIN: 9160-7024
Scopus Author ID: 57222273391
Researcher ID: AAD-2073-2019

Cand. Sci. (Tech)

Rússia, Петрозаводск; Москва

Anton Vladzymyrskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: a.vladzimirsky@npcmr.ru
ORCID ID: 0000-0002-2990-7736
Código SPIN: 3602-7120
Scopus Author ID: 8944262100
Researcher ID: D-1447-2017
Rússia, Moscow

Dariya Sharova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: d.sharova@npcmr.ru
ORCID ID: 0000-0001-5792-3912
Código SPIN: 1811-7595
Rússia, Moscow

Kirill Arzamasov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: k.arzamasov@npcmr.ru
ORCID ID: 0000-0001-7786-0349
Código SPIN: 3160-8062
Rússia, Moscow

Aleksander Khramov

Innopolis University; Immanuel Kant Baltic Federal University

Email: a.hramov@innopolis.ru
ORCID ID: 0000-0003-2787-2530
Código SPIN: 7357-7556
Scopus Author ID: 34834
Rússia, Kazan; Kaliningrad

Bibliografia

  1. Deep medicine: how artificial intelligence can make healthcare human again by eric topol. New York: Basic Books; 2019. 341 p.
  2. Roberts M, Driggs D, Thorpe M, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021;3(3):199–217. doi: 10.1038/s42256-021-00307-0
  3. Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi: 10.1136/bmj.m1328
  4. Gusev AV, Morozov SP, Kutischev VA, Novitsky RE. Regulatory and legal regulation of software for healthcare created using artificial intelligence technologies in the Russian Federation. Medical Technologies. Evaluation Selection. 2021;(1):36–45. (In Russ). doi: 10.17116/medtech20214301136
  5. Sharova DE, Zinchenko VV, Akhmad ES, et al. On the question of ethical aspects of the introduction of artificial intelligence systems in healthcare. Digital Diagnostics. 2021;2(3):356–368. (In Russ). doi: 10.17816/DD77446
  6. Morozov SP, Zinchenko VV, Khoruzhaya AN, et al. Standardization of artificial intelligence in healthcare: Russia is becoming a leader. Doctor and information technology. 2021;(2):12–19. (In Russ). doi: 10.25881/18110193_2021_2_12
  7. Morozov SP, Vladzimirsky AV, Sharova DE, et al. The first national standards of the Russian Federation for artificial intelligence systems in medicine. Quality Management Med. 2022;(1):58–62. (In Russ).
  8. Komar PA, Dmitriev VS, Ledneva AM, et al. Rating of artificial intelligence startups: prospects for Russian healthcare. Russ J Telemedicine E-Health. 2021;7(3)32–41. (In Russ). doi: 10.29188/2712-9217-2021-7-3-32-41
  9. Korsakov SN. The outline of a new method of research using machines comparing ideas. Transl. with French. Ed. by A.S. Mikhailov. Moscow: Moscow Institute of Engineering and Physics; 2009. 44 c.
  10. Gavrilova TA, Khoroshevsky VF. Knowledge bases of intelligent systems: study guide. Saint Petersburg: Piter; 2000. 384 p.
  11. Danilov VV, Proutski A, Karpovsky A, et al. Indirect supervision applied to COVID-19 and pneumonia classification. Informatics Medicine Unlocked. 2022;28:100835. doi: 10.1016/j.imu.2021.100835
  12. Mohammadi R, Salehi M, Ghaffari H, et al. Transfer learning-based automatic detection of coronavirus disease 2019 (COVID-19) from chest X-ray images. J Biomed Phys Eng. 2020;10(5) 559–568. doi: 10.31661/jbpe.v0i0.2008-1153
  13. Neroev VV, Bragin AA, Zaitseva OV. Development of a prototype service for the diagnosis of diabetic retinopathy from fundus images using artificial intelligence methods. National Healthcare. 2021;2(2):64–72. (In Russ). doi: 10.47093/2713-069X.2021.2.2.64-7
  14. Nevzorova VA, Brodskaya TA, Shakhgeldyan KI, et al. Machine learning methods in predicting the risks of 5-year mortality (according to the ESSE-RF study in Primorsky Krai). Cardiovascular Therapy Prevention. 2022;21(1):2908. (In Russ). doi: 10.15829/1728-8800-2022-2908
  15. Gilyarevsky SR, Gavrilov DV, Gusev AV. The results of a retrospective analysis of records of electronic outpatient medical records of patients with chronic heart failure: the first Russian experience. Russ J Cardiology. 2021;26(5):4502. (In Russ). doi: 10.15829/1560-4071-2021-4502
  16. Karpov OE, Grubov VV, Maksimenko VA, et al. Noise amplification precedes extreme epileptic events on human EEG. Physical Review. 2021;103:022310. doi: 10.1103/PhysRevE.103.022310
  17. Kuchin AS, Grubov VV, Maksimenko VA, Utyashev NP. Automated workplace of an epileptologist with the possibility of automatic search for epilepsy attacks. Doctor and information technology. 2021;(3):62–73. (In Russ). doi: 1025881/18110193_2021_3_62
  18. Kuc A, Korchagin S, Maksimenko VA, et al. Combining statistical analysis and machine learning for eeg scalp topograms classification. Frontiers Systems Neuroscience. 2021;15:716897. doi: 10.3389/fnsys.2021.716897
  19. Hramov AE, Maksimenko VA, Pisarchik AN. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports. 2021;918:1–133. doi: 10.1016/j.physrep.2021.03.002
  20. Morozov SP, Vladzimirsky AV, Shulkin IM, et al. Investigation of the feasibility of using artificial intelligence technologies in radiation diagnostics. Doctor and information technology. 2022;(1):12–29. (In Russ).

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar

Declaração de direitos autorais © Eco-vector, 2022

Creative Commons License
Este artigo é disponível sob a Licença Creative Commons Atribuição–NãoComercial–SemDerivações 4.0 Internacional.

Este site utiliza cookies

Ao continuar usando nosso site, você concorda com o procedimento de cookies que mantêm o site funcionando normalmente.

Informação sobre cookies