俄罗斯联邦保健事业人工智能技术领域的研发发展：2021年结果

封面图片

如何引用文章

全文:

详细

人工智能技术在俄罗斯保健事业的应用是我国人工智能发展国家战略的优先领域之一。在医疗机构中引入基于人工智能的数字解决方案应有助于提高生活水平和医疗救护质量，包括预防性检查、基于图像分析的诊断、预测疾病的发生和发展、选择最佳的药物剂量、减少流行病的威胁、自动化和提高手术干预的准确性等。

人工智能在医疗保健中的应用领域的规范和技术规定正在发展。相关解决方案的国内市场已经建立，其中一些已获得 俄罗斯联邦卫生监督局的医疗器械注册证书。各个科学团队开展研究工作。与此同时，我们在人工智能领域仍显着落后于美国、中国等领先国家。2021年对医疗保健人工智能产品的投资显着下降。至少从市场指标来看，滞后的主要原因在于公共卫生组织资助人工智能项目的需求和能力较低，以及对此类解决方案的安全性和有效性的信任。

作者简介

Aleksander V. Gusev

K-Skai; Russian Research Institute of Health

编辑信件的主要联系方式.
Email: agusev@webiomed.ai
ORCID iD: 0000-0002-7380-8460
SPIN 代码: 9160-7024
Scopus 作者 ID: 57222273391
Researcher ID: AAD-2073-2019

Cand. Sci. (Tech)

俄罗斯联邦, Petrozavodsk; Moscow

Anton V. Vladzymyrskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: a.vladzimirsky@npcmr.ru
ORCID iD: 0000-0002-2990-7736
SPIN 代码: 3602-7120
Scopus 作者 ID: 8944262100
Researcher ID: D-1447-2017
俄罗斯联邦, Moscow

Dariya E. Sharova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: d.sharova@npcmr.ru
ORCID iD: 0000-0001-5792-3912
SPIN 代码: 1811-7595
俄罗斯联邦, Moscow

Kirill M. Arzamasov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: k.arzamasov@npcmr.ru
ORCID iD: 0000-0001-7786-0349
SPIN 代码: 3160-8062
俄罗斯联邦, Moscow

Aleksander E. Khramov

Innopolis University; Immanuel Kant Baltic Federal University

Email: a.hramov@innopolis.ru
ORCID iD: 0000-0003-2787-2530
SPIN 代码: 7357-7556
Scopus 作者 ID: 34834
俄罗斯联邦, Kazan; Kaliningrad

参考

  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).

补充文件

附件文件
动作
1. JATS XML
下载
2. 图1。根据CB Insights的数据，用于医疗卫生的人工智能系统的风险投资动态为十亿美元。

下载 (105KB)
索引源数据
3. 图2。过去10年，俄罗斯作者在医学和人工智能的交叉领域发表的Scopus科学信息数据库中的索引出版物数量。 注：数据库查询： (TITLE-ABS-KEY (medicine OR healthcare) AND ("artificial intelligence" OR "machine learning") AND AFFILCOUNTRY (Russia OR "Russian Federation")).

下载 (82KB)
索引源数据
4. 图3。2015-2021年俄罗斯对医学和医疗保健人工智能系统的投资动态（作者数据），百万卢布

下载 (74KB)
索引源数据
5. 图6。2017-2020年俄罗斯医学和医疗保健人工智能系统开发商的收入动态（作者数据），百万卢布

下载 (63KB)
索引源数据
6. 图4。俄罗斯对医学和医疗保健人工智能系统的投资来源（作者数据），百万卢布

下载 (66KB)
索引源数据
7. 图5。投资医疗和医疗保健人工智能系统的方向（作者数据），百万卢布 注：NTI——国家技术倡议基金会；RFPI——俄罗斯直接投资基金；FRII——互联网倡议发展基金会。

下载 (153KB)
索引源数据

版权所有 © Eco-Vector, 2022

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

##common.cookie##