Journal article
AI-induced deskilling in medicine: a mixed-method review and research agenda for healthcare and beyond
- Abstract:
- The integration of Artificial Intelligence (AI) in healthcare is reshaping clinical practice, offering both opportunities for enhanced decision-making and risks of skill degradation among medical professionals. This growing impact calls for a comprehensive evaluation of its effects on medical expertise. This study presents a mixed-method literature review, combining systematic analysis with narrative synthesis to examine AI-induced deskilling and upskilling inhibition-the erosion of medical expertise and the reduction of opportunities for skill acquisition due to AI-driven decision support systems. Anchoring the discussion in the core medical competencies outlined by the Federation of Royal Colleges of Physicians of the UK-Practical Assessment of Clinical Examination Skills (PACESMRCPUK), the systematic review identifies key vulnerabilities in physical examination, differential diagnosis, clinical judgment, and physician-patient communication. The narrative review explores broader themes related to Human–AI Interaction and the Impact of AI on Human Skills in Organizations. In response to concerns about the Second Singularity-a scenario in which decision-making autonomy is increasingly ceded to AI, weakening human oversight-this review advocates for a research agenda that prioritizes longitudinal studies, real-time monitoring of AI’s impact, and the development of frameworks to mitigate skill erosion, ensuring the preservation of professional autonomy and the safeguarding of the irreplaceable elements of human judgment in medicine and beyond.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.9MB, Terms of use)
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- Publisher copy:
- 10.1007/s10462-025-11352-1
Authors
+ State Secretariat for Education, Research and Innovation
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- Funder identifier:
- https://ror.org/00mt8k932
- Grant:
- 2024.0002
- Programme:
- Swiss Government Excellence Scholarship (ESKAS)
+ European Union
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- Funder identifier:
- https://ror.org/019w4f821
- Grant:
- H53D23008090001
- Publisher:
- Springer
- Journal:
- Artificial Intelligence Review More from this journal
- Volume:
- 58
- Issue:
- 11
- Article number:
- 356
- Publication date:
- 2025-08-27
- Acceptance date:
- 2025-08-04
- DOI:
- EISSN:
-
1573-7462
- ISSN:
-
0269-2821
- Language:
-
English
- Keywords:
- Pubs id:
-
2378269
- Local pid:
-
pubs:2378269
- Deposit date:
-
2026-04-28
- ARK identifier:
Terms of use
- Copyright holder:
- Natali et al.
- Copyright date:
- 2025
- Rights statement:
- © The Author(s) 2025. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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