The impact of digitalization on the professional landscape of the medical industry: labor market needs and development prospects (analysis of foreign experience)

Significance. The introduction of digital technologies into the healthcare system has a profound and multidimensional impact on its most valuable and vulnerable component - human resources. Digital transformation is changing the landscape of professional activity, leading to the emergence of fundamentally new specialties and requirements for competencies and skills of medical professionals.

Aim: to systematize current data on the impact of digitalization on the demand for medical personnel, the changing professional landscape, and the requirements for the competencies of medical professionals.

Materials and methods. We systematically analyzed international publications retrieved from Scopus, PubMed, and Google Scholar using the search terms "digital technologies," "artificial intelligence," or "telemedicine," "workforce," and "healthcare.". All types of studies assessing the impact of digital technologies (artificial intelligence, telemedicine, robotics, the Internet of Medical Things, and big data analysis) on the workload of medical staff were included.

Results. The review includes 61 international publications, which indicate that, as of the end of 2025, digital technologies do not offer the global healthcare system a solution to the workforce shortage. Instead, they provide a set of tools enabling it to function more efficiently, sustainably, and efficiently, even in the face of an objective global human resource shortage. Therefore, digitalization is considered a key tool in mitigating the consequences of the global workforce shortage in the majority of publications included in the review.

Conclusion. The review highlights the potential for a profound transformation of the medical profession landscape. physicians and nurses are evolving toward data management, critical interpretation, and enhanced patient engagement. There is a strong demand for fundamentally new hybrid professions at the intersection of medicine, information technology, and data science (bioinformaticians, medical software developers, cybersecurity specialists). Realizing the potential of digitalization requires overcoming regulatory barriers and significantly transforming the medical education system.

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.