1. Ukaz Prezidenta Rossijskoj Federacii ot 10.10.2019 №490 «O razvitii iskusstvennogo intellekta v Rossijskoj Federacii» // Elektronnyj fond pravovyh i normativno-tekhnicheskih dokumentov. Available at: https://docs.cntd.ru/document/563441794. Accessed 28.08.2023. (In Russ.)
2. Gusev AV, Vladzymyrskyy AV, Sharova DE, et al. Evolution of research and development in the field of artificial intelligence technologies for healthcare in the Russian Federation: results of 2021 // Digital Diagnostics. 2022; 3(3): 178-194. (In Russ.) doi: 10.17816/DD107367.
3. Arzamasov KM, Vasilev YuA, Vladzymyrskyy AV, et al. The use of computer vision for the mammography preventive research. Profilakticheskaja-medicina. 2023; 26(6): 117-123. (In Russ.) doi: 10.17116/profmed202326061117.
4. Pavlov NA, et al. Reference medical datasets (MosMedData) for independent external evaluation of algorithms based on artificial intelligence in diagnostics. Digital Diagnostics. 2021; 2(1): 49-66. (In Russ.) doi: 10.17816/DD60635.
5. GOST R 52653-2006. Informacionno-kommunikacionnye tekhnologii v obrazovanii. Terminy i opredeleniya // Elektronnyj fond pravovyh i normativno-tekhnicheskih dokumentov. Available at: https://docs.cntd.ru/document/1200053103. Accessed 28.08.2023. (In Russ.)
6. Willemink MJ, Koszek WA, Hardell C, et al. Preparing Medical Imaging Data for Machine Learning. Radiology. 2020; 295(1): 4-15. doi:10.1148/radiol.2020192224.
7. Aggarwal R, Sounderajah V, Martin G, et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med. 2021; 4(1): 65. doi:10.1038/s41746-021-00438-z.
8. Vladzimirskiy AV. Vasil’ev YUA, et al. Computer vision in radiation diagnostics: the first stage of the Moscow experiment. M.: Izdatel’skie resheniya, 2022; 388 р. (In Russ.)
9. Vasiliev YuA, et al. Fundamental principles for standardizing and systematizing information about data sets for machine learning in medical diagnostics. Healthcare Manager. 2023; 4: 28-41. (In Russ.) doi: 10.21045/1811-0185-2023-4-28-41.
10. Prikaz Ministerstva zdravoohraneniya Rossijskoj Federacii ot 24.12.2018 №911n «Ob utverzhdenii Trebovanij k gosudarstvennym informacionnym sistemam v sfere zdravoohraneniya sub»ektov Rossijskoj Federacii, medicinskim informacionnym sistemam medicinskih organizacij i informacionnym sistemam farmacevticheskih organizacij». Available at: https://normativ.kontur.ru/document?moduleId=1&documentId=338271. Accessed 28.08.2023. (In Russ.)
11. Federal’nyj zakon «O personal’nyh dannyh» ot 27.07.2006 №152-FZ. Available at: https://normativ.kontur.ru/document?moduleId=1&documentId=447363. Accessed 28.08.2023. (In Russ.)
12. Kulberg NS, et al. Methodology and tools for creating training samples for artificial intelligence systems for recognizing lung cancer on CT images. Healthcare of the Russian Federation. 2020; 6: 343-350. (In Russ.) doi: 10.46563/0044-197X-2020-64-6-343-350.
13. Borisov AA, et al. Using transfer learning for automated detection of defects in chest X-rays. Medical imaging. 2023; 27(1): 158-168. (In Russ.) doi: 10.24835/1607-0763-1243.
14. Amelina EV, et al. Features of creating a database of neuro-oncological 3D MRI images for training artificial intelligence. Siberian Scientific Medical Journal. 2022; 42(6): 51-59. (In Russ.) doi: 10.18699/SSMJ20220606.
15. Kivelev YuV, et al. Formation of a big data set for clinical research using the example of cerebral aneurysms. 2023; 43(3): 86-94. (In Russ.) doi: 10.18699/SSMJ20230311.
16. Nguyen HQ, Lam K, Le LT, et al. VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations. Sci Data. 2022; 9(1): 429. doi: 10.1038/s41597-022-01498-w.