Коэволюция систем сбора данных, наборов данных и технологий искусственного интеллекта в хирургии

Наука о хирургических данных (Surgical Data Science, SDS) – дисциплина, направленная на повышение качества хирургической помощи через сбор, организацию, анализ и моделирование данных. Показано, что прогресс науки о хирургических данных можно определить коэволюцией трех ее компонентов: систем сбора интраоперационных данных, наборов данных, на которых обучаются алгоритмы искусственного интеллекта (ИИ), и самих алгоритмов. В настоящем исследовании предложена авторская концептуальная периодизация этой коэволюции, охватывающая период с середины 2010-х гг. – времени институционального оформления науки о хирургических данных, – до настоящего времени. Она включает три этапа: этап проприетарных интегрированных операционных (≈середина 2010-х гг. – 2019), характеризующийся закрытостью систем сбора данных; этап обхода ограничений проприетарных систем (≈2019 – 2024); и этап интероперабельности и фонового искусственного интеллекта (≈2024 – наст.вр.). Параллельно прослеживается периодизация наборов данных, создаваемых на каждом из этих этапов, – от мономодальных и многоканальных к масштабным мультимодальным наборам данных. Показано, что каждый этап исходно определялся системами сбора данных, которые задавали границы качества синхронизации, состава модальностей и создаваемых наборов данных, тогда как потребности алгоритмов ИИ определяли направление архитектурной эволюции систем. Поиск проведён по PubMed, IEEE Xplore, arXiv, материалам конференций MICCAI и CVPR, официальным сайтам производителей и технической документации. Из 127 первично выявленных публикаций после скрининга и полнотекстового анализа в работу включено 68 научных публикаций, дополненных 29 источниками технической документации и официальных сайтов производителей (всего 97 источников). Ключевыми факторами дальнейшего прогресса станут принятие и применение стандартов интероперабельности медицинских устройств, развитие мультимодальных наборов данных, фундаментальных ИИ-моделей и мультиагентных архитектур, их внедрение в пространство интегрированной операционной.

1. Wauben LSGL, van Grevenstein WMU, Goossens RHM, van der Meulen FH, Lange JF. Operative notes do not reflect reality in laparoscopic cholecystectomy. Br J Surg. 2011; 98(10): 1431-1436. doi: 10.1002/bjs.7576.

2. Maier-Hein L, Vedula SS, Speidel S, et al. Surgical data science for next-generation interventions. Nat Biomed Eng. 2017; 1: 691-696. doi: 10.1038/s41551-017-0132-7.

3. Maier-Hein L, Eisenmann M, Sarikaya D, et al. Surgical data science – from concepts toward clinical translation. Med Image Anal. 2022; 76: 102306. doi: 10.1016/j.media.2021.102306.

4. Pan Y, Zou SH, Yang JW, Si WX, Zheng WM. Surgical data science in time-critical contexts: a roadmap toward brain-inspired computing. J Comput Sci Technol. 2026; 41(1): 428-446. doi: 10.1007/s11390- 025-5908-8.

5. Campbell K, Gardner A, Scott DJ, Johnson J, Harvey J, Kazley A. Interprofessional staff perspectives on the adoption of or black box technology and simulations to improve patient safety: a multi-methods survey. Adv Simul. 2023; 8: 24. doi: 10.1186/s41077-023-00263-2.

6. Al Abbas AI, Meier J, Daniel W, et al. Impact of team performance on the surgical safety checklist on patient outcomes: an operating room black box analysis. Surg Endosc. 2024; 38(10): 5613-5622. doi: 10.1007/s00464-024-11064-7.

7. Møller KE, Sørensen JL, Topperzer MK, et al. Implementation of an Innovative Technology Called the OR Black Box: A Feasibility Study. Surg Innov. 2023; 30(1): 64-72. doi: 10.1177/15533506221106258.

8. Van Dalen ASHM, Jansen M, van Haperen M, et al. Implementing structured team debriefing using a black box in the operating room: surveying team satisfaction. Surg Endosc. 2021; 35(3): 1406-1419. doi: 10.1007/s00464-020-07526-3.

9. Gordon L, Reed C, Sorensen JL, et al. Perceptions of safety culture and recording in the operating room: understanding barriers to video data capture. Surg Endosc. 2022; 36(6): 3789-3797. doi: 10.1007/s00464-021-08695-5.

10. Al Abbas AI, Sankaranarayanan G, Polanco PM, et al. The operating room black box: understanding adherence to surgical checklists. Ann Surg. 2022; 276(6): 995-1001. doi: 10.1097/SLA.0000000000005695.

11. Arnrich B, Denecke C, Faraj L, et al. Continuous multimodal AI with wearable vital signs predicts postoperative complications in the general ward [preprint]. medRxiv. 2025. doi: 10.1101/2025.11.25.25340950.

12. Bain AP, Succar B, Odeh J, et al. Automated tagging of intraoperative physiologic events: understanding the clinical consequences. Surg Endosc. 2026; 40(3): 1431-1438. doi: 10.1007/s00464-025-12409-6.

13. Huaulmé A, Harada K, Nguyen QM, et al. PEg TRAnsfer workflow recognition challenge report: do multimodal data improve recognition? Comput Methods Programs Biomed. 2023; 236: 107561. doi: 10.1016/j.cmpb.2023.107561.

14. Данилов Г.В., Потапов А.А., Шифрин М.А., и др. Исследование осложнений в нейрохирургии с помощью технологий искусственного интеллекта // Российский нейрохирургический журнал имени профессора А.Л. Поленова. – 2022. – №14(1-1). – С.44-47.

15. Кобринский Б.А. Системы искусственного интеллекта в хирургии: возможности, ограничения и перспективы. Обзор литературы // Российский вестник детской хирургии, анестезиологии и реаниматологии. – 2023. – №13(3). – С.385-404. doi: 10.17816/psaic1547.

16. Whittemore R, Knafl K. The integrative review: updated methodology. J Adv Nurs. 2005; 52(5): 546- 553. doi: 10.1111/j.1365-2648.2005.03621.x.

17. Stryker. Connected OR Operating System [Internet]. [cited 2026 May 20]. Available from: https://www.stryker.com/us/en/communications/systems/-isuite-experience/connected-or-operatingsystem.html

18. Caresyntax. Surgical Intelligence Platform. Available from: https://www.caresyntax.com. Сited 2026 May 20.

19. Surgical Safety Technologies Inc. OR Black Box®. Available from: https://www.surgicalsafety.com. Сited 2026 May 20.

20. Ibach B, et al. Integrating medical devices in the operating room using service-oriented architectures. Biomed Tech (Berl). 2012; 57(4): 221-228. doi: 10.1515/bmt-2011-0101.

21. Rockstroh M, Wittig M, Franke S, Meixensberger J, Neumuth T. Video-based detection of device interaction in the operating room. Biomed Tech (Berl). 2016; 61(5): 567-576. doi: 10.1515/bmt-2015-0008.

22. Bergh B, Radermacher K, editors. OR.NET – Secure and dynamic networking in operating room and hospital: update 2016. Lübeck: Universität zu Lübeck; 2016. Available from: https://www.isp.uniluebeck.de/sites/default/files/content/projects/ornet/ornet-2016-brochure.pdf. Сited 2026 May 2.

23. OR.NET e.V. SDC History: Development of the IEEE 11073 SDC standards. Leipzig: OR.NET e.V.; 2016. Available from: https://ornet.org/?page_id=5942&lang=en#top. Сited 2026 May 6.

24. The OpenICE Team (MD PnP Program). OpenICE User Introduction and System Component Overview. Available from: https://www.openice.info/2015. Сited 2026 May 5.

25. IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems. *IEEE Std 1588-2019*. IEEE; 2020. doi: 10.1109/IEEESTD.2020.9129227.

26. Taquet L, Conway BJ, Boerger TF, et al. Synchronization of kinetic and kinematic hand tasks with electrocorticogra-phy and cortical stimulation during awake craniotomies. PLoS One. 2023; 18(3): e0283460. doi: 10.1371/journal.pone.0283460.

27. Pérez J, Mena L, Villarroel JL. Time synchronization in modular collaborative robots using PTP and ROS 2. arXiv:1809.07295. 2018. Available from: https://arxiv.org/abs/1809.07295. Сited 2026 May 2.

28. Schwaner KL, Iturrate I, Andersen JKH, Dam CR, Jensen PT, Savarimuthu TR. MOPS: a modular and open platform for surgical robotics research. In: 2021 International Symposium on Medical Robotics (ISMR). IEEE; 2021. Р.1-8. doi: 10.1109/ISMR48346.2021.9661539.

29. Quarez J, Li Y, Irzan H, et al. MUTUAL: Towards Holistic Sensing and Inference in the Operating Room. In: Celebi ME, Reyes M, Chen Z, Li X, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 Workshops. Cham: Springer; 2025. Р.1-12. (Lecture Notes in Computer Science; Vol. 15274). doi: 10.1007/978-3-031-77610-6_17.

30. Turrin BB, Kassab Junior F, Vieira JE, Sameshima K. Data extraction from operating room equipment: an engineer-ing and data science approach to general anesthesia monitoring. IEEE Access. 2025; 13: 184159-184175. doi: 10.1109/ACCESS.2025.11208630.

31. National Instruments. cDAQ-9189 Specifications. 2025. Available from: https://www.ni.com/docs/en-US/bundle/cdaq-9189-specs/page/specs.html. Сited 2026 May 3.

32. OR-X. Unlocking New Horizons in Surgical Data Science: The ORD Initiative. Available from: https://or-x.ch/en/research/projects/unlocking-new-horizons-in-surgical-data-science-the-ord-initiative. Сited 2026 May 20.

33. Schorp V, et al. A Modular Edge Device Network for Surgery Digitalization [Internet]. arXiv:2503.14049. 2025. Avail-able from: https://arxiv.org/abs/2503.14049. Сited 2026 May 3.

34. NVIDIA. NVIDIA Holoscan Documentation. Available from: https://docs.nvidia.com/holoscan. Сited 2026 May 3.

35. Project MONAI. MONAI: AI Toolkit for Healthcare Imaging [Internet]. GitHub; 2019. Available from: https://github.com/Project-MONAI/MONAI. Сited 2026 May 3.

36. IEEE 11073-10701-2022, IEEE Standard for Health Informatics – Device Interoperability – Part 10701: Point-of-Care Medical Device Communication – Metric Provisioning by Participants in a Service- Oriented Device Connectivity (SDC) System. IEEE. 2023: 1-85. doi: 10.1109/IEEESTD.2023.10263987.

37. ISO/IEEE 11073-10700-2024. ISO/IEEE International Standard: Health informatics--Device interoperability--Part 10700: Point-of-care medical device communication--Standard for base requirements for participants in a Service-oriented Device Connectivity (SDC) system. IEEE; 2024.

38. ISO/IEC/IEEE DIS 8802-1AS. Information technology – Telecommunications and information exchange between systems – Local and metropolitan area networks – Part 1AS: Timing and synchronization for time-sensitive applica-tions in bridged local area networks. In development. Stage: 40.99, 2026.

39. NVIDIA. Easily build edge AI apps with dynamic flow control in NVIDIA Holoscan 3.0. Available from: https://developer.nvidia.com/blog/easily-build-edge-ai-apps-with-dynamic-flow-control-in-nvidiaholoscan-3-0/. Сited 2026 May 5.

40. Barco. Barco unveils at GTC 2025. Available from: https://www.barco.com/ja/about/pressreleases/2025/barco-unveils-at-gtc-2025. Сited 2026 May 5.

41. ADLINK Technology. ADLINK edge AI platforms with NVIDIA Thor. Available from: https://www.adlinktech.com.cn/en/News/adlink-edge-ai-platforms-nvidia-thor. Сited 2026 May 5.

42. Yuan High-Tech. Yuan news content 163. Available from: https://www.yuan.com.tw/newscontent/163. Сited 2026 May 5.

43. NVIDIA. NVIDIA IGX Thor processor for physical AI in industrial, medical and edge. Available from: https://blogs.nvidia.com/blog/igx-thor-processor-physical-ai-industrial-medical-edge/. Сited 2026 May 5.

44. NVIDIA. NVIDIA IGX Thor powers industrial, medical and robotics edge AI applications. Available from: https://developer.nvidia.com/blog/nvidia-igx-thor-powers-industrial-medical-and-robotics-edge-aiapplications/. Сited 2026 May 5.

45. Signify Research. What’s next for clinical care: Signify Research’s 2026 predictions. Available from: https://www.signifyresearch.net/insights/whats-next-for-clinical-care-signify-researchs-2026- predictions/. Сited 2026 May 5.

46. Maier-Hein L, Eisenmann M, Feldmann C, et al. Surgical Data Science: A Consensus Perspective. arXiv: 1806.03184. 2018. doi: 10.48550/arXiv.1806.03184.

47. Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N. EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos. IEEE transactions on medical imaging. 36(1): 86-97. doi: 10.1109/TMI.2016.2593957.

48. Srivastav V, Issenhuth T, Abdolrahim K, et al. MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation. arXiv: 1808.08180. 2018. Сited 2026 May 5. doi: 10.48550/arXiv.1808.08180.

49. Gao Y, Vedula SS, Reiley CE, et al. JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS): A Surgical Activity Dataset for Human Motion Modeling [Internet]. Johns Hopkins University; 2014. Available from: https://cirl.lcsr.jhu.edu/wp-content/uploads/dlm_uploads/2014/09/JIGSAWS.pdf. Сited 2026 May 5.

50. Nwoye CI, Elgohary K, Srinivas A, et al. CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools. arXiv: 2312.07352. 2023. Сited 2026 May 5. doi: 10.48550/arXiv.2312.07352.

51. Goodman ED, Patel KK, Zhang Y, et al. Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning. JAMA Surg. 2024. doi: 10.1001/jamasurg.2023.6262.

52. Özsoy E, Piatkowska E, Ilic F, et al. 4D-OR: Semantic Scene Graphs for OR Domain Modeling. arXiv; 2022 Mar 22. Available from: https://arxiv.org/abs/2203.11937. Сited 2026 May 5. doi: 10.48550/arXiv.2203.11937.

53. Özsoy E, Czempiel T, Holm F, Pellegrini C, Navab N. LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms. In: MICCAI 2023. Springer; 2023. doi: 10.1007/978-3-031-43996-4_29.

54. Özsoy E, Pellegrini C, Czempiel T, et al. MM-OR: A Large Multimodal Operating Room Dataset for Semantic Under-standing of High-Intensity Surgical Environments. In: CVPR 2025. IEEE; 2025. Available from: https://openaccess.thecvf.com/content/CVPR2025/papers/Ozsoy_MM-OR_A_Large_Multimodal_Operating_Room_Dataset_for_Semantic_Understanding_CVPR_2025_paper.pdf.

55. Özsoy E, Pellegrini C, Czempiel T, et al. MM-OR: Official code and dataset for “MM-OR: A Large Multimodal Operat-ing Room Dataset for Semantic Understanding of High-Intensity Surgical Environments” (CVPR 2025). GitHub; 2025. Available from: https://github.com/egeozsoy/MM-OR. Сited 2026 May 5.

56. Choi TM, Kim S, Lee J, et al. SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding. arXiv; 2025 Nov 26. Available from: https://arxiv.org/abs/2511.21339. Сited 2026 May 5. doi: 10.48550/arXiv.2511.21339.

57. Yuan K, Srivastav V, Yu T, et al. Learning multi-modal representations by watching hundreds of surgical video lec-tures. Med Image Anal. 2025. doi: 10.48550/arXiv.2307.15220.

58. Perez A, Li M, Wang H, et al. SurgLaVi: Large-Scale Hierarchical Dataset for Surgical Vision- Language Representation Learning [Internet]. arXiv; 2025 Sep 9. Available from: https://arxiv.org/ abs/2509.10555. Сited 2026 May 5. doi: 10.48550/arXiv.2509.10555.

59. Gastager D, Ghazaei G, Patsch C. Watch and Learn: Leveraging Expert Knowledge and Language for Surgical Video Understanding. arXiv; 2025 Mar 14. Available from: https://arxiv.org/abs/2503.11392. Сited 2026 May 5. doi: 10.48550/arXiv.2503.11392.

60. Zeng Z, Chen X, Wang Y, et al. SurgΣ: A Spectrum of Large-Scale Multimodal Data and Foundation Models for Surgi-cal Intelligence. arXiv; 2026 Mar 17. Available from: https://arxiv.org/abs/2603.16822. Сited 2026 May 5. doi: 10.48550/arXiv.2603.16822.

61. Xu M, Wang L, Chen Y, et al. Generalized Recognition of Basic Surgical Actions Enables Skill Assessment and Vision-Language-Model-based Surgical Planning. arXiv; 2026 Mar 13. Available from: https:// arxiv.org/abs/2603.12787. Сited 2026 May 5. doi: 10.48550/arXiv.2603.12787.

62. Özsoy E, Pellegrini C, Bani-Harouni D, et al. Specialized Foundation Models for Intelligent Operating Rooms. arXiv; 2025 May 19. Available from: https://arxiv.org/abs/2505.12890. Сited 2026 May 25. doi: 10.48550/arXiv.2505.12890.

63. Wang G, Li Y, Chen Z, et al. EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery. arXiv; 2025 Jan 20. Available from: https://arxiv.org/abs/2501.11347. Сited 2026 May 5. doi: 10.48550/arXiv.2501.11347.

64. Hao P, Li Z, Wang Y, et al. Surgery-R1: Advancing Surgical-VQLA with Reasoning Multimodal Large Language Model via Reinforcement Learning. arXiv; 2025 Jun 24. Available from: https://arxiv.org/ abs/2506.19469. Сited 2026 May 5. doi: 10.48550/arXiv.2506.19469.

65. Wang G, Mo W, Wang J, et al. SurgVidLM: Towards Multi-grained Surgical Video Understanding with Large Lan-guage Model. arXiv; 2025 Jun 22. Available from: https://arxiv.org/abs/2506.17873. Сited 2026 May 5. doi: 10.48550/arXiv.2506.17873.

66. Skobelev K, Ivanov A, Petrov S, et al. A Comparative Study in Surgical AI: Potential and Limitations of Data, Com-pute, and Scaling. medRxiv; 2026. Available from: https://www.medrxiv.org/content/10.1101/2026.02.18.26345936v1. Сited 2026 May 25. doi: 10.1101/2026.02.18.26345936.

67. Wu YC, Yin M, Shi B, et al. MedOS: AI-XR-Cobot World Model for Clinical Perception and Action. medRxiv; 2026. Available from: https://www.medrxiv.org/content/10.64898/2026.02.18.26345936v1. Сited 2026 May 25. doi: 10.64898/2026.02.18.26345936.

68. He Y, Chen Z, Wang L, et al. Cosmos-H-Surgical: Learning Surgical Robot Policies from Videos via World Modeling. arXiv; 2025 Dec 29. Available from: https://arxiv.org/abs/2512.23162. Сited 2026 May 5. doi: 10.48550/arXiv.2512.23162.

69. Paranjape JN, Sikder S, Patel VM, Vedula SS. Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos. arXiv; 2023 Aug 7. Available from: https://arxiv.org/abs/2308.04035. Сited 2026 May 5. doi: 10.48550/arXiv.2308.04035.

70. Eckhoff JA, Rosman G, Altieri MS, Speidel S, Stoyanov D, Anvari M, et al. SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improve-ment, and surgical education). Surg Endosc. 2023 Nov; 37(11): 8690-8707. doi: 10.1007/s00464-023-10288-3.

71. King A, Fowler GE, Macefield RC, Walker H, Thomas C, Markar S, et al. Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review. BJS Open. 2025 Aug; 9(4): zraf073. doi: 10.1093/bjsopen/zraf073.

72. Strong JS, Yura M, Takeuchi M, et al. External Validation of an Automated Surgical Step Recognition Model for Ro-botic Distal Gastrectomy Using a Multicenter Dataset. Ann Gastroenterol Surg. 2025; 9: 1155-1162. doi: 10.1002/ags3.70042.

73. MLCommons Medical Working Group. Comprehensive Open Federated Ecosystem (COFE) showcased at MICCAI 2024 [Internet]. MLCommons; 2024 Nov 21. Available from: https://mlcommons.org/2024/11/medical-miccai-cofe. Сited 2026 May 5.

74. Kirchner M, Schmidt A, Weber L, et al. Federated EndoViT: Pretraining Vision Transformers via Federated Learning on Endoscopic Image Collections. arXiv; 2025 Apr 23. Available from: https://arxiv.org/abs/2504.16612. Сited 2026 May 5. doi: 10.48550/arXiv.2504.16612.

75. Fang Z, Liu Y, Wang H, et al. SurgFed: Language-guided Multi-Task Federated Learning for Surgical Video Under-standing. arXiv; 2026 Mar 10. Available from: https://arxiv.org/abs/2603.09496. Сited 2026 May 5. doi: 10.48550/arXiv.2603.09496.

76. Fang Z, Wang L, Chen Y, et al. Spatio-Temporal Representation Decoupling and Enhancement for Federated In-strument Segmentation in Surgical Videos. arXiv; 2025 Jun 30. Available from: https://arxiv.org/abs/2506.23759. Сited 2026 May 5. doi: 10.48550/arXiv.2506.23759.

77. Beede E, Baylor E, Hersch F, et al. A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery; 2020: 1-12. doi: 10.1145/3313831.3376718.

78. Карпов О.Э., Андриков Д.А., Максименко В.А., Храмов А.Е. Прозрачный искусственный интеллект для медицины // Врач и информационные технологии. – 2022. – №2. – С.4-11. doi: 10.25881/18110193_2022_2_4.

79. Jung JJ, Jüni P, Lebovic G, Grantcharov T. First-year Analysis of the Operating Room Black Box Study. Ann Surg. 2020; 271(1): 122-127. doi: 10.1097/SLA.0000000000002863.

80. Campbell KK, Abreu AA, Zeh HJ, et al. Using OR Black Box Technology to Determine Quality Improvement Out-comes for In-situ Timeout and Debrief Simulation. Ann Surg. 2026; 283(1): 122- 129. doi: 10.1097/SLA.0000000000006438.

81. Artisight. Smart Hospital Platform for the Future of Care. Chicago (IL): Artisight. Available from: https://artisight.com/about/. Сited 2026 May 25.

82. GO FAIR International. FAIR Principles. 2017 Nov 23. Available from: https://www.go-fair.org/fairprinciples/. Сited 2026 May 25.

83. OR.NET e.V. Interoperabilität für Medizingeräte – Medical Device Communication [Internet]. Leipzig: OR.NET e.V. Available from: https://ornet.org/. Сited 2026 May 5.

84. Kasparick M, Schmitz M, Andersen B, et al. OR.NET: a service-oriented architecture for safe and dynamic medical device interoperability. Biomed Tech (Berl). 2018; 63(1): 11-30. doi: 10.1515/bmt-2017-0020.

85. Yilmaz O, Stegemann D, Radermacher K, Lange M, Janß A. Integrating machine-readable user interface require-ments into open networked operating rooms. Front Digit Health. 2025; 7: 1520584. doi: 10.3389/fdgth.2025.1520584.

86. OR-X Translational Center for Surgery. Leading the Future: Translational Center for Surgery Switzerland. Zurich: Balgrist University Hospital. Available from: https://or-x.ch/en/translationalcenter-for-surgery/. Сited 2026 May 25.

87. University of Zurich. Advancing Surgical Data Science: The OR-X Open Research Data Initiative. Open Science UZH; 2024. Available from: https://www.openscience.uzh.ch/en/open-science-at-uzh/p5-open-science-projects/projects-2021-2024/or-x.html. Сited 2026 May 25.

88. Oh N, Jung KH, Choi GS. Physical AI goes to the operating room: are we ready for the Surgical Data Factory? Ann Surg Treat Res. 2026; 110(3): 135-143. doi: 10.4174/astr.2026.110.3.135.

89. Xinhua News Agency. China Focus: China advances AI-powered digital healthcare for higher medical efficiency, ac-cessibility. 2026 Mar 15. Available from: https://english.news.cn/20260315/a98d00f541974687b852e24268c7a111/c.html. Сited 2026 May 25.

90. Japan Society for Endoscopic Surgery (JSES). Surgical Data Science Committee. Tokyo: JSES; 2026 Apr 23. Available from: https://www.jses.or.jp/modules/about/index.php?content_id=59. Сited 2026 May 25.

91. Maity D, Satish R, Dharmaraju R, et al. MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India. BMC Med Inform Decis Mak. 2025; 25: 252. doi: 10.1186/s12911-025-03092-7.

92. Амелина Е.В., Летягин А.Ю., Тучинов Б.Н., и др. Особенности создания базы данных нейроонкологических 3D МРТ-изображений для обучения искусственного интеллекта // Сибирский научный медицинский журнал. – 2022. – №42(6). – С.51-59. doi: 10.18699/SSMJ20220606.

93. Сеченовский Университет. В России создается первый публичный датасет интенсивной терапии РИКОРД: 5300 клинических случаев для развития отечественных ИИ-решений в реанимации. 2026. Доступно по: https://www.sechenov.ru/pressroom/news/v-rossii-sozdaetsya-pervyypublichnyy-dataset-intensivnoy-terapii-rikord-5300-klinicheskikh-sluchaev/ Ссылка действительна на 05.06.2026.

94. DiViSy Group. DiViSy: интегральные решения для современных медицинских центров и боль- ниц. Москва: DiViSy Group. Доступно по: https://divisy.biz/. Ссылка действительна на 26.05.2026.

95. Medical Visual Systems. MVS Vega: интегрированная операционная. Санкт-Петербург: Medical Visual Systems. Доступно по: https://mvsystem.ru/mvs-vega/. Ссылка действительна на 26.05.2026.

96. Кобякова О.С., Ерёмченко О.А., Куракова Н.Г. Технологии искусственного интеллекта в здравоохранении в глобальной патентной экосистеме: 2000–2023 гг. // Врач и информационные технологии. – 2024. – №1. – С.44-59. doi: 10.25881/18110193_2024_1_44.

97. Кобякова О.С., Канев А.Ф., Куракова Н.Г., Кармина Р.Л. Влияние цифровизации на профессиональный ландшафт медицинской отрасли: потребности рынка труда и перспективы развития (анализ зарубежного опыта) // Врач и информационные технологии. – 2026. –№1. – С.38-51. doi: 10.25881/18110193_2026_1_38.