AI in the clinic: the risk of physician deskilling and strategies for its mitigation

Introduction: The rapid integration of artificial intelligence (AI) systems into healthcare, while improving diagnostic and therapeutic efficiency, raises serious concerns regarding the long-term impact on physicians’ professional competencies. A central risk is deskilling – the gradual and irreversible loss of clinical skills resulting from excessive reliance on automated systems.

Objective: To analyze the potential mechanisms of AI-induced deskilling, assess the feasibility of its objective diagnosis, and propose possible solutions to this problem.

Materials and Methods: This brief review examines latest scientific publications addressing the influence of AI on physicians’ professional activities. The analysis focuses on three key aspects of clinical practice: physical examination, diagnosis, and patient communication. Additionally, a specific empirical case is examined in detail, in which objective quantitative metrics were employed to assess skill degradation.

Results: The key mechanisms of AI’s negative impact were identified, including the degradation of manual skills, the «illusion of accuracy», «automation-induced bias», and the weakening of diagnostic reasoning, empathy, and moral competence. The evidence presented indicates that prolonged AI use can lead to a statistically significant decline in clinical performance, as substantiated by objective data such as a reduced adenoma detection rate during colonoscopy. Proposed solutions encompass the transformation of medical education, optimization of physician workflow, implementation of automated attention-monitoring systems, and a transition toward personalized competency maintenance programs.

Conclusion: Minimizing the risks of deskilling requires a systematic, rather than ad hoc, integration of AI into medicine, grounded in principles of ethics and pedagogical soundness. The central task is to create an environment in which technology serves to augment, rather than replace, human expertise, thereby preserving the balance between technological progress and the humanistic essence of medical practice.

1. Karpov O.E, Hramov A.E. Informacionnye tekhnologii, vychislitel'nye sistemy i iskusstvennyj intellekt v medicine. M.: DPK-Press, 2022. (In Russ.)

2. Natali C., Marconi L., Dias Duran L.D., Cabitza F. AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond. Artificial Intelligence Review. 2025; 58(11): 1-40.

3. Berzin T.M., Topol E.J. Preserving clinical skills in the age of AI assistance. The Lancet. 2025; 406(10513): 1719.

4. Tatel C.E., Ackerman P.L. Procedural skill retention and decay: A meta-analytic review. Psychological Bulletin. 2025.

5. Sparrow R., Hatherley J.J. The promise and perils of AI in medicine. Int J Chin Compar Philos Med. 2019; 17(2): 79-109.

6. Michels M.E., Evans D.E., Blok G.A. What is a clinical skill? Searching for order in chaos through a modified Delphi process. Med Teach. 2012; 34(8): e573-e581.

7. Bainbridge L. Ironies of automation. In: Analysis, design and evaluation of man–machine systems. Elsevier. 1983: 129-135.

8. Anichini G., Natali C., Cabitza F. Invisible to machines: designing ai that supports vision work in radiology. In: Computer supported cooperative work (CSCW). 2024: 1-44.

9. Simpkin A.L., Vyas J.M., Armstrong K.A. Diagnostic reasoning: an endangered competency in internal medicine training. Annals of Internal Medicine. 2017; 167(7): 507-508.

10. Smith P.J., Baumann E. Human-automation teaming: unintended consequences of automation on user performance. In: 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). 2020: 1-9.

11. Elder A., McManus C., McAlpine L., Dacre J. What skills are tested in the new paces examination? Ann Acad Med Singapore. 2011; 40(3): 119.

12. Cabitza F., Rasoini R., Gensini G.F. Unintended consequences of machine learning in medicine. JAMA. 2017; 318(6): 517-518.

13. Kashou A.H., Noseworthy P.A., Anavekar N.S., Rowlandson I., May A.M. Bridging ecg learning with emerging technologies: advancing clinical excellence. J Electrocardiol. 2024; 86: 153765.

14. Rafner J., Dellermann D., Hjorth A., Veraszto D., Kampf C., MacKay W., Sherson J. Deskilling, upskilling, and reskilling: a case for hybrid intelligence. Morals Mach. 2022; 1(2): 24-39.

15. Lu J. Will medical technology deskill doctors? Int Educ Stud. 2016; 9(7): 130-134.

16. Parchmann N., Hansen D., Orzechowski M., Steger F. An ethical assessment of professional opinions on concerns, chances, and limitations of the implementation of an artificial intelligence-based technology into the geriatric patient treatment and continuity of care. Geroscience. 2024; 46(6): 6269-6282.

17. Duran L.D.D. Deskilling of medical professionals: an unintended consequence of ai implementation? Giornale di filosofia. 2021; 2(2): 47-59.

18. Vallor S. Moral Deskilling and Upskilling in a New Machine Age: Reflections on the Ambiguous Future of Character. Philosophy & Technology. 2015; 28(1): 107-124.

19. Budzyń K., Romańczyk M., Kitala D., Kołodziej P., Bugajski M., Adami H.O., Mori Y. Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study. The Lancet Gastroenterology & Hepatology. 2025; 10(10): 896-903.

20. Zulkipli I.N., Alam F., Lim M-A. Integrating AI in medical education: embracing ethical usage and critical understanding. Front Med. 2023; 10: 1279707.

21. Ackerman P.L., Tatel C.E. Resolving problems with the skill retention literature: An empirical demonstration and recommendations for researchers. Journal of Experimental Psychology: Applied. 2023.

22. Aslam T.M., Hoyle D.C. Translating the machine: skills that human clinicians must develop in the era of artificial intelligence. Ophthalmology and therapy. 2022; 11(1): 69-80.

23. Vankov V.V., Artemova O. R., Karpov O.E., Matvienko A.V., Gusev A.V., Enikeev I.M., Kostina E.V. Results of the implementation of artificial intelligence in the Russian healthcare. Medical doctor and information technology. 2024; 3: 32-43. (In Russ.) doi: 10.25881/18110193_2024_3_32.