Оценка надежности и эффективности систем искусственного интеллекта в лучевой диагностике на этапе эксплуатации
Работая с сайтом, я даю свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта обрабатывается системой Яндекс.Метрика
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Assessment of the reliability and effectiveness of artificial intelligence systems in radiation diagnostics at the operational stage

idZinchenko V.V., idVladzimirskyy A.V., idArzamasov K.M.

UDC 004.8
DOI: 10.26102/2310-6018/2025.49.2.016

  • Abstract
  • List of references
  • About authors

In the context of the active implementation of artificial intelligence (AI) technologies in healthcare, ensuring stable, controlled and high-quality operation of such systems at the operational stage is of particular relevance. Monitoring of AI systems is enshrined in law: within three years after the implementation of medical devices, including AI systems, it is necessary to provide regular reports to regulatory authorities. The aim of the study is to develop methods for assessing the reliability and effectiveness of medical artificial intelligence for radiation diagnostics. The proposed methods were tested on the data of the Moscow Experiment on the use of innovative technologies in the field of computer vision in the direction of chest radiography, collected in 2023. The developed methods take into account a set of parameters: emerging technological defects, research processing time, the degree of agreement of doctors with the analysis results and other indicators. The proposed approach can be adapted for various types of medical research and become the basis for a comprehensive assessment of AI systems as part of the monitoring of medical devices with artificial intelligence. The implementation of these methods can increase the level of trust of the medical community not only in specific AI-based solutions, but also in intelligent technologies in healthcare in general.

1. Ball H.C. Improving Healthcare Cost, Quality, and Access, Through Artificial Intelligence and Machine Learning Applications. Journal of Healthcare Management. 2021;66(4):271–279. https://doi.org/10.1097/JHM-D-21-00149

2. Chen M., Decary M. Artificial Intelligence in Healthcare: An Essential Guide for Health Leaders. Healthcare Management Forum. 2019;33(1):10–18. https://doi.org/10.1177/0840470419873123

3. Vokinger K.N., Feuerriegel S., Kesselheim A.S. Continual Learning in Medical Devices: FDA's Action Plan and Beyond. The Lancet. 2021;3(6):E337–E338. https://doi.org/10.1016/S2589-7500(21)00076-5

4. Casado F.E., Lema D., Criado M.F., Iglesias R., Regueiro C.V., Barro S. Concept Drift Detection and Adaptation for Federated and Continual Learning. Multimedia Tools and Applications. 2022;81(3):3397–3419. https://doi.org/10.1007/s11042-021-11219-x

5. Pemberton H.G., Zaki L.A.M., Goodkin O., et al. Technical and Clinical Validation of Commercial Automated Volumetric MRI Tools for Dementia Diagnosis – a Systematic Review. Neuroradiology. 2021;63(11):1773–1789. https://doi.org/10.1007/s00234-021-02746-3

6. Nomura Yu., Miki S., Hayashi N., et al. Novel Platform for Development, Training, and Validation of Computer-Assisted Detection/Diagnosis Software. International Journal of Computer Assisted Radiology and Surgery. 2020;15(4):661–672. https://doi.org/10.1007/s11548-020-02132-z

7. Chetverikov S.F., Arzamasov K.M., Andreichenko A.E., Novik V.P., Bobrovskaya T.M., Vladzimirsky A.V. Approaches to Sampling for Quality Control of Artificial Intelligence in Biomedical Research. Modern Technologies in Medicine. 2023;15(2):19–27. (In Russ.). https://doi.org/10.17691/stm2023.15.2.02

8. Harvey H.B., Gowda V. How the FDA Regulates AI. Academic Radiology. 2020;27(1):58–61. https://doi.org/10.1016/j.acra.2019.09.017

9. Sounderajah V., Ashrafian H., Golub R.M., et al. Developing a Reporting Guideline for Artificial Intelligence-Centred Diagnostic Test Accuracy Studies: the STARD-AI Protocol. BMJ Open. 2021;11(6). https://doi.org/10.1136/bmjopen-2020-047709

10. Khinvasara T., Tzenios N., Shankar A. Post-Market Surveillance of Medical Devices Using AI. Journal of Complementary and Alternative Medical Research. 2024;25(7):108–122. https://doi.org/10.9734/jocamr/2024/v25i7552

11. Lyell D., Wang Yi., Coiera E., Magrabi F. More Than Algorithms: an Analysis of Safety Events Involving ML-Enabled Medical Devices Reported to the FDA. Journal of the American Medical Informatics Association. 2023;30(7):1227–1236. https://doi.org/10.1093/jamia/ocad065

12. Feng J., Phillips R.V., Malenica I., et al. Clinical Artificial Intelligence Quality Improvement: Towards Continual Monitoring and Updating of AI Algorithms in Healthcare. npj Digital Medicine. 2022;5(1). https://doi.org/10.1038/s41746-022-00611-y

13. Vasilev Yu.A., Zinchenko V.V., Kudryavtsev N.D., Mikhailova A.A., Klyashtorny V.G., Vladzymyrskyy A.V. Radiologists' Satisfaction and Engagement with Artificial Intelligence Software. Medical Doctor and IT. 2024;(1):70–81. (In Russ.). https://doi.org/10.25881/18110193_2024_1_70

Zinchenko Victoria Valerievna

Email: ZinchenkoVV1@zdrav.mos.ru

ORCID |

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

Moscow, Russian Federation

Vladzimirskyy Anton Vyacheslavovich
Doctor of Medical Sciences

ORCID |

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

Moscow, Russian Federation

Arzamasov Kirill Mikhailovich
Candidate of Medical Sciences, Docent

ORCID |

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department
MIREA - Russian Technological University

Moscow, Russian Federation

Keywords: artificial intelligence, reliability, efficiency, artificial intelligence system, radiology, radiation diagnostics, monitoring

For citation: Zinchenko V.V., Vladzimirskyy A.V., Arzamasov K.M. Assessment of the reliability and effectiveness of artificial intelligence systems in radiation diagnostics at the operational stage. Modeling, Optimization and Information Technology. 2025;13(2). URL: https://moitvivt.ru/ru/journal/pdf?id=1886 DOI: 10.26102/2310-6018/2025.49.2.016 (In Russ).

27

Full text in PDF

Received 04.04.2025

Revised 22.04.2025

Accepted 28.04.2025