Transformation of the medical workers’ work landscape under the influence of artificial intelligence

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Abstract

Introduction. The use of artificial intelligence (AI) in medicine is increasingly being applied. Modernization and digitalization processes are taking place in all levels and structures, including in the primary health care sector. The task is to create and ensure the possibility of effective management of work activities, taking into account the acquisition of new knowledge and competencies through the development of a retraining system. The management of this system should take into account the needs of medical professionals and the convenience of obtaining the necessary skills.The purpose of the article is to study the specifics of the existing work landscape and trends in the development of educational trajectories of medical workers.Materials and methods. The study includes an analysis of the results of secondary data presented in the monitoring of research agencies for the possibility of using OLAP technology. The author’s research is aimed at analyzing the responses of the survey respondents (n = 1499). Visualization of the landscape of labor activity is implemented using graph theory.Results. Modern technologies in medicine significantly affect the work of medical workers. Knowledge in the field of AI and IT is becoming more and more in demand. The system of medical retraining, taking into account new work formats, requires significant rethinking. Based on OLAP technologies and graph theory, the authors propose an author’s model of the work landscape of a health worker.Research limitations. The study has regional (Moscow) limitations. The analysis uses statistical data and the results of a mass questionnaire survey in medical institutions of the Moscow Department of Health.Conclusions. Taking into account existing trends and changes in work activity (including the introduction of AI), it is advisable in the near future to review both approaches to training medical workers and to provide an opportunity to gain new knowledge for the implementation of innovative personnel strategies corresponding to the "New Moscow Standards" nationwide.Compliance with ethical standards. The study was approved by the Ethical Committee for the Examination of Research in the field of public health, organization and sociology of healthcare at the Research Institute for Healthcare Organization and Medical Management (Protocol No. 03-01/EC/2023, 14.03.2023).Contribution of the authors: Medvedeva E.I. — concept and design of the study, writing the article, editing; Kroshilin S.V. — collection and processing of material, statistical processing, writing an article. All authors — approval of the final version of the article, responsibility for the integrity of the article.Funding. This article was prepared by the author’s team within the framework of the research "Scientific and methodological support of organizational aspects of improving the accessibility and quality of medical care in the public health system of Moscow" (No. according to EGISU: 123032100063-3).Conflict of interest. The authors declare the absence of obvious and potential conflicts of interest in connection with the publication of this article.Received: April 4, 2025 / Accepted: June 24, 2025 / Published: October 31, 2025

About the authors

Elena I. Medvedeva

Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department

Email: e_lenam@mail.ru
ORCID iD: 0000-0003-4200-1047

Sergey V. Kroshilin

Research Institute for Healthcare Organization and Medical Management of Moscow Healthcare Department

Email: krosh_sergey@mail.ru
ORCID iD: 0000-0002-6070-1234

References

  1. Wiegand T., Krishnamurthy R., Kuglitsch M., Lee N., Pujari S., Salathé M., et al. WHO and ITU establish benchmarking process for artificial intelligence in health. Lancet. 2019; 394(10192): 9–11. https://doi.org/10.1016/S0140-6736(19)30762-7
  2. Аксенова Е.И., Медведева Е.И., Крошилин С.В. Использование цифровых технологий в деятельности медицинских организаций. Здравоохранение Российской Федерации. 2024; 68(5): 356–63. https://doi.org/10.47470/0044-197X-2024-68-5-356-363 https://elibrary.ru/iueudm
  3. Pragmatic Coders. Lech E. 19 must-know digital healthcare market trends for 2025; 2023. Available at: https://pragmaticcoders.com/blog/healthcare-tech-trends-digital-health-trends
  4. Rigby M.J. Ethical dimensions of using artificial intelligence in health care. AMA J. Ethics. 2019; 21(2): 121–4. https://doi.org/10.1001/amajethics.2019.121
  5. Старшинин А.В., Аксенова Е.И., Бурковская Ю.В. Стратегии и концепция развития цифровизации в здравоохранении в разных странах мира. М.; 2024.
  6. Старшинин А.В., Медведева Е.И., Крошилин С.В. Трансформация процесса управления кадрами: рестайлинг обязанностей немедицинских работников. Здоровье мегаполиса. 2023: 4(2): 60–72. https://doi.org/10.47619/2713-2617.zm.2023.v.4i2;60-72 https://elibrary.ru/kitedu
  7. Аксенова Е.И., Камынина Н.Н., Старшинин А.В., Нечаев О.И., Крюкова И.А., Кузнецов М.Ю. Образовательный проект как инструмент развития метанавыков у специалистов московского здравоохранения: на примере проекта «Научная лаборатория: Московская поликлиника». Проблемы социальной гигиены, здравоохранения и истории медицины. 2024; 32(S2): 1042–6. https://doi.org/10.32687/0869-866X-2024-32-s2-1042-1046 https://elibrary.ru/zdcffm
  8. Ouyang F., Jiao P. Artificial Intelligence in Education: The Three Paradigms. Comput. Educ.: Artif. Intell. 2021; 2: 100020. https://doi.org/10.1016/j.caeai.2021.100020
  9. Nabibayova G. Expanding the intellectual capabilities of OLAP technology using neural networks. Problems of Information Society. 2024; 15(2): 43–8. https://doi.org/10.25045/jpis.v15.i2.05
  10. Чемидова А.Б., Суворов С.В., Царькова Н.И., Жиляева И.А. Агрегация показателей в Olap-кубе. Российский экономический интернет-журнал. 2019; (4): 138–53. https://elibrary.ru/xftsua
  11. Arul Sh.M., Senthil G., Jayasudha S., Alkhayyat A., Khalikov A., Elangovan R. Graph theory and algorithms for network analysis. E3S Web Conf. 2023; 399: 10. https://doi.org/10.1051/e3sconf/202339908002

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