Влияние цифровизации на профессиональный ландшафт медицинской отрасли: потребности рынка труда и перспективы развития (анализ зарубежного опыта)

Актуальность. Внедрение цифровых технологий в систему здравоохранения оказывает глубокое и многовекторное влияние на самую ценную и уязвимую ее составляющую – человеческие ресурсы. Цифровая трансформация меняет ландшафт профессиональной деятельности, инициируя появление принципиально новых специальностей и требований к компетенциям и навыкам медицинских работников.

Цель исследования: систематизировать актуальные данные о влиянии цифровизации на потребность в медицинских кадрах, на изменение профессионального ландшафта и на требования к компетенциям медицинских работников.

Материалы и методы. Систематически проанализированы зарубежные публикации, извлеченные из баз данных Scopus, PubMed, Google Scholar с использованием поискового образа: «digital technologies» OR «artificial intelligence» or «telemedicine» and «workforce» and «healthcare». Включались все типы исследований, оценивающих влияние цифровых технологий (искусственный интеллект, телемедицина, роботизация, интернет медицинских вещей и анализ больших данных) на уровень нагрузки на медперсонал.

Результаты. В обзор вошла 61 зарубежная публикация, позволяющая отметить, что по состоянию на конец 2025 г. цифровые технологии не предлагают глобальной системе здравоохранения решения проблемы кадрового дефицита, но предоставляют комплекс инструментов, позволяющих ей функционировать более эффективно, устойчиво и качественно даже в условиях объективного глобального дефицита человеческих ресурсов. Поэтому цифровизация в большинстве публикаций, вошедших в обзор, рассматривается в качестве ключевого инструмента смягчения последствий глобального кадрового дефицита.

Заключение. Выполненный обзор обозначил перспективу глубинной трансформации ландшафта медицинских профессий. Роли врачей и медсестер эволюционируют в сторону управления данными, их критической интерпретации и усиленного взаимодействия с пациентом. Возникает устойчивый спрос на принципиально новые гибридные профессии на стыке медицины, информационных технологий и наук о данных (биоинформатики, разработчики медицинского программного обеспечения, специалисты по кибербезопасности). Для реализации потенциала цифровизации необходимы преодоление нормативных барьеров и значительная трансформация системы медицинского образования.

1. Socha-Dietrich, K. Empowering the health workforce to make the most of the digital revolution. OECD Health Working Papers. 2021; 129. doi: 10.1787/37ff0eaa-en.

2. WHO guideline on health workforce development, attraction, recruitment and retention in rural and remote areas. 10.09.2025.

3. Ag Ahmed MA, Diakité SL, Sissoko K, Gagnon MP, Charron S. Factors explaining the shortage and poor retention of qualified health workers in rural and remote areas of the Kayes, region of Mali: a qualitative study. Rural Remote Health. 2020; 20(3):5772. doi: 10.22605/RRH5772.

4. Yeung AWK, Torkamani A, Butte AJ, et al. The promise of digital healthcare technologies. Front Public Health. 2023; 11: 1196596. doi: 10.3389/fpubh.2023.1196596.

5. Al-Saleem AI, Aldakheel MK. Barriers to Workforce-Driven Innovation in Healthcare. Cureus. 2024; 16(10): e72316. doi: 10.7759/cureus.72316.

6. Al-Haimi B, Ali F, Hujainah F. Digital Transformation in Healthcare: Impact on Organizations' Strategies, Future Landscape, and Required Skills. In: Navigating the Intersection of Business, Sustainability and Technology. Contributions to Environmental Sciences & Innovative Business Technology. Springer, Singapore. 2023. doi: 10.1007/978-981-99-8572-2_3.

7. Jeilani A, Hussein A. Impact of digital health technologies adoption on healthcare workers’ performance and workload: perspective with DOI and TOE models. BMC Health Serv Res 25, 271(2025). doi: 10.1186/s12913-025-12414-4.

8. Mohd J, Abid H, Ravi PS. Health informatics to enhance the healthcare industry's culture: An extensive analysis of its features, contributions, applications and limitations. Informatics and Health. 2024; 1(2): 123-148. doi: 10.1016/j.infoh.2024.05.001.

9. Zheng T, Lin F, Li X, et al. Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study. EClinicalMedicine. 2023; 58: 101913. doi: 10.1016/j.eclinm.2023.101913.

10. Raya-Povedano JL, Romero-Martín S, Elías-Cabot E, et al. AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation. Radiology. 2021; 300(1): 57-65. doi: 10.1148/radiol.2021203555.

11. Yacoub B, Varga-Szemes A, Schoepf UJ, et al. Impact of Artificial Intelligence Assistance on Chest CT Interpretation Times: A Prospective Randomized Study. AJR Am J Roentgenol. 2022; 219(5): 743-751. doi: 10.2214/AJR.22.27598.

12. Li N, Wu Z, Jiang C, Sun L, et al. An automatic fresh rib fracture detection and positioning system using deep learning. Br J Radiol. 2023; 96(1146): 20221006. doi: 10.1259/bjr.20221006.

13. Shi Z, Miao C, Schoepf UJ, et al. A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images. Nat Commun. 2020; 11(1): 6090. doi: 10.1038/s41467-020-19527-w.

14. Ni Q, Sun ZY, Qi L, et al. A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images. Eur Radiol. 2020; 30(12): 6517-6527. doi: 10.1007/s00330-020-07044-9.

15. Lancaster HL, Zheng S, Aleshina OO, et al. Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification. Lung Cancer. 2022; 165: 133-140. doi: 10.1016/j.lungcan.2022.01.002.

16. Wenderott K, Krups J, Luetkens JA, et al. Prospective effects of an artificial intelligence-based computeraided detection system for prostate imaging on routine workflow and radiologists' outcomes. Eur J Radiol. 2024; 170: 111252. doi: 10.1016/j.ejrad.2023.111252.

17. Yang R, Yan C, Lu S, et al. Tracking cancer lesions on surgical samples of gastric cancer by artificial intelligent algorithms. J Cancer. 2021; 12: 6473-83.

18. Eloy C, Marques A, Pinto J, et al. Artificial intelligence-assisted cancer diagnosis improves the efficiency of pathologists in prostatic biopsies. Virchows Arch. 2023; 482: 595-604.

19. Peltola J, Basnyat P, Armand Larsen S, et al. Semiautomated classification of nocturnal seizures using video recordings. Epilepsia. 2023; 64(Suppl 4): S65-71.

20. Katz BZ, Feldman MD, Tessema M, et al. Evaluation of Scopio Labs X100 Full Field PBS: the first highresolution full field viewing of peripheral blood specimens combined with artificial intelligence-based morphological analysis. Int J Lab Hematol. 2021; 43: 1408-16.

21. Yan Y, Jiang W, Zhou Y, et al. Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images. Front Med (Lausanne). 2023; 10: 1164188.

22. Yang Y, Pan J, Yuan M, et al. Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study. Ann Transl Med. 2022; 10: 1088.

23. Tancredi C, Ibba S, Fantesini A, et al. Capturing patient voices: A focus group-based study unveiling the potential of AI in medical diagnosis. Human Technology. 2024; 20(3): 541-557. doi: 10.14254/1795-6889.2024.20-3.6.

24. Alì M, Fantesini A, Morcella MT, et al. Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges. Critical Reviews™ in Oncogenesis. 2024; 29(2).

25. Bongurala AR, Save D, Virmani A, Kashyap R. Transforming health care with artificial intelligence: redefining medical documentation. Mayo Clin Proc Digit Health. 2024; 2(3): 342-347.

26. Căvescu AM, Popescu N. Predictive Analytics in Human Resources Management: Evaluating AIHR’s Role in Talent Retention. AppliedMath. 2025; 5: 99. doi: 10.3390/appliedmath5030099.

27. Fraser Health Advances AI in Healthcare with Digital Navigator and Scheduling Innovations. https://www.startupecosystem.ca/news/fraser-health-advances-ai-in-healthcare-with-digital-navigator-andscheduling-innovations/

28. Ministry of Health Israel. Government of Israel, Ministry of Health. National Plan for Digital. https://www.health.gov.il/About/projects/DigitalHealth/Pages/default.aspx.

29. Rojas SL, Ashok M, Morss DyS, et al. Contextual Frameworks for Research on the Implementation of Complex System Interventions [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US). 2014 Mar. Patient-Centered Medical Home Framework. https://www.ncbi.nlm.nih.gov/books/NBK196203/

30. Stoumpos AI, Kitsios F, Talias MA. Digital Transformation in Healthcare: Technology Acceptance and Its Applications. Int J Environ Res Public Health. 2023; 20(4): 3407. doi: 10.3390/ijerph20043407.

31. Liu X, Keane PA, Denniston AK. Time to regenerate: the doctor in the age of artificial intelligence. J R Soc Med. 2018; 111(4): 113-116. doi: 10.1177/0141076818762648.

32. https://medical.sectra.com/case/ai-frees-up-valuable-time-for-radiologists-in-a-swedish-healthcareregion/

33. Qure.ai impact stories. The impact of AI at The Royal Bolton NHS Hospital, UK. https://www.qure.ai/impact_stories/the-impact-of-ai-at-the-royal-bolton-nhs-hospital-uk.

34. Qure.ai impact stories. On Ground with IHVN and Fujifilm in Nigeria. https://www.qure.ai/impact_stories/on-ground-with-ihvn-and-fujifilm-in-nigeria.

35. Zou FW, Tang YF, Liu CY, Ma JA, Hu CH. Concordance Study Between IBM Watson for Oncology and Real Clinical Practice for Cervical Cancer Patients in China: A Retrospective Analysis. Front Genet. 2020; 11: 200. doi: 10.3389/fgene.2020.00200.

36. Siemens Healthineers. «Облачное решение teamplay — приложения для управления производительностью». https://www.siemens-healthineers.com/ru/digital-health-solutions/digital-solutionsoverview/service-line-managment-solutions/teamplay.

37. Suki.ai. The AI infrastructure for halthcare. https://www.suki.ai/

38. BD Pyxis MedStation ES System. Automated dispensing cabinet for single and multi-facilities medication management. https://www.bd.com/en-uk/products-and-solutions/products/productfamilies/bd-pyxis-medstation-es-system.

39. Jung YY, Walsh Á, Patel J, Lai K. Benefits and challenges associated with implementation and ongoing use of automated dispensing cabinet for medicines: a scoping review. Explor Res Clin Soc Pharm. 2025; 18: 100599.

40. Philips eCareCoordinator Clinical dashboard for ambulatory health. https://www.philips.cz/healthcare/product/HCNOCTN482/ecarecoordinator-clinical-dashboard-for-ambulatory-health.

41. Global strategy on digital health 2020-2025. Geneva: World Health Organization; 2021.

42. Whitepaper “unlocking healthcare's future: the invaluable role of clinical informatics”. https://www. imss.org/sites/hde/files/media/file/2024/04/18/wp_value-of-clinical-informatics-1.pdf.

43. Gyldenkærne C, Hansen JU, Hertzum M, Mønsted T. Innovation tactics for implementing an ML application in healthcare: a long and winding road. Int J Hum Comput Stud. 2024; 181: 103162

44. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021.

45. Topol, E.J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25: 44-56. doi: 10.1038/s41591-018-0300-7.

46. https://www.techtarget.com/searchhealthit/definition/clinical-informatics.

47. Hamilton A. The Future of Artificial Intelligence in Surgery. Cureus. 2024; 16(7): e63699. doi: 10.7759/cureus.63699.

48. Gou F, Liu J, Xiao C, Wu J. Research on artificial-intelligence-assisted medicine: a survey on medical artificial intelligence. Diagnostics (Basel). 2024; 14: 1472.

49. Klumpp M, Hintze M, Immonen M, et al. Artificial intelligence for hospital health care: application cases and answers to challenges in European hospitals. Healthcare (Basel). 2021; 9: 961.

50. Closing the digital skills gap in healthcare POLICY BRIEF 72 Identifying core digital skills and competencies and education and training opportunities for health professionals in the European Union. https://eurohealthobservatory.who.int/docs/librariesprovider3/publicationsnew/policybriefbewell-digital-v3-30042025.pdf?sfvrsn=5533673_2.

51. Jose A, Tortorella GL, Vassolo R, Kumar M, Mac Cawley AF. Professional Competence and Its Effect on the Implementation of Healthcare 4.0 Technologies: Scoping Review and Future Research Directions. Int J Environ Res Public Health. 2022; 20(1): 478. doi: 10.3390/ijerph20010478.

52. How to Automate Repetitive Tasks in Healthcare. Automation in healthcare industry. https://www.aalpha.net/blog/how-to-automate-repetitive-tasks-in-healthcare/

53. Yıldırım Ş, Yücekaya AD, Hekimoğlu M, et al. AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector. Appl. Sci. 2025$ 15^ 6282. doi: 10.3390/app15116282.

54. Shahmoradi L, Safdari R, Ahmadi H, Zahmatkeshan M. Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects. Med J Islam Repub Iran. 2021; 35: 27. doi: 10.47176/mjiri.35.27.

55. Kakhi K, Jagatheesaperumal SK, Khosravi A, Alizadehsani R, Acharya UR. Fatigue monitoring using wearables and AI: Trends, challenges, and future opportunities. Comput Biol Med. 2025; 195: 110461. doi: 10.1016/j.compbiomed.2025.

56. Bernburg M, Gebhardt JS, Groneberg DA, Mache S. Impact of Digitalization in Dentistry on Technostress, Mental Health, and Job Satisfaction: A Quantitative Study. Healthcare (Basel). 2025; 13(1): 72. doi: 10.3390/healthcare13010072.

57. Biro JM, Handley JL, Malcolm McCurry J, et al. Opportunities and risks of artificial intelligence in patient portal messaging in primary care. NPJ Digit Med. 2025; 8(1): 222. doi: 10.1038/s41746-025-01586-2.

58. Wirkkala M, Wijk K, Larsson AC, Engström M. Technology frustration in healthcare - does it matter in staff ratings of stress, emotional exhaustion, and satisfaction with care? A cross-sectional correlational study using the job demands-resources theory. BMC Health Serv Res. 2024; 24(1): 1557. doi: 10.1186/s12913-024-11906-z.

59. Board on Health Care Services; Institute of Medicine. The Role of Telehealth in an Evolving Health Care Environment: Workshop Summary. Washington (DC): National Academies Press (US). 2012; 4. Available from: https://www.ncbi.nlm.nih.gov/books/NBK207146/

60. Jones C, Thornton J, Wyatt JC. Artificial intelligence and clinical decision support: clinicians' perspectives on trust, trustworthiness, and liability. Med Law Rev. 2023; 31(4): 501-520. doi: 10.1093/medlaw/fwad013.

61. Tolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review. JMIR Med Educ. 2024; 10: e54793. doi: 10.2196/54793.