Artificial intelligence and deskilling in medicine
Open accessAbstract
Artificial intelligence is increasingly being used in medical practice to complete tasks that were previously completed by the physician, such as visit documentation, treatment plans and discharge summaries. As artificial intelligence becomes a routine part of medical care, physicians increasingly trust and rely on its clinical recommendations. However, there is concern that some physicians, especially those younger and less experienced, will become over-reliant on artificial intelligence. Over-reliance on it may reduce the quality of clinical reasoning and decision-making, negatively impact patient communications and raise the potential for deskilling. As artificial intelligence becomes a routine part of medical treatment, it is imperative that physicians recognise the limitations of artificial intelligence tools. These tools may assist with basic administrative tasks but cannot replace the uniquely human interpersonal and reasoning skills of physicians. The purpose of this feature article is to discuss the risks of physician deskilling based on increasing reliance on artificial intelligence.
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Such physicians may perform well in well-resourced settings where AI is reliable but struggle in resource-limited settings, during system failures, or in emergency conditions.
“Such physicians may perform well in well-resourced settings where AI is reliable but struggle in resource-limited settings, during system failures, or in emergency conditions 39,40 .”
From AI-induced never-skilling in medical education (2026) · cites this paper as [40]
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AI-induced never-skilling in medical education
Ke, Y., Jin, L., Ong, J. C. L., Thirunavukarasu, A. J., Car, J., Cheung, C. Y., Tham, Y. C., Ting, D. S. W., Ong, M. E. H., Compton, S., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04438-y · Cited by 35 · see citing works
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“Additionally, reliance on automated GenAI outputs also raises the risk of cognitive deskilling, whereby higher-order pedagogical reasoning and instructional decision-making may be diminished through overautomation rather than being exercised and refined [10,11].”
- Authors
- Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E. D., Bauer, R., & Bauer, M.
- Affiliations
- Michigan State University (US)
- ChronoRecord Association
- University of Oxford (GB)
- University of California, Los Angeles (US)
- Western Michigan University (US)
- University Hospital Carl Gustav Carus (DE)
- Corresponding author
- Monteith, Scott
- Venue
- The British Journal of Psychiatry
- Publisher
- Royal College of Psychiatrists
- Published
- 2026-01-08
- Pages
- 1-3
- ISSN
- 0007-1250 (ISSN-L)
- 1472-1465
- OpenAlex
- W7118577692
- Cited by
- 26
- References
- 8
- Citations by year
- 2026: 26
- FWCI
- 16.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The British Journal of Psychiatry (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in healthcare, deskilling, clinical reasoning, clinical decision-making, trust in AI, physician-patient communication
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