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Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills
Open accessAbstract
BACKGROUND: Alongside the promise of improving clinical work, advances in healthcare artificial intelligence (AI) raise concerns about the risk of deskilling clinicians. This purpose of this study is to examine the issue of deskilling from the perspective of diverse group of professional stakeholders with knowledge and/or experiences in the development, deployment and regulation of healthcare AI. METHODS: We conducted qualitative, semi-structured interviews with 72 professionals with AI expertise and/or professional or clinical expertise who were involved in development, deployment and/or regulation of healthcare AI. Data analysis using combined constructivist grounded theory and framework approach was performed concurrently with data collection. FINDINGS: Our analysis showed participants had diverse views on three contentious issues regarding AI and deskilling. The first involved competing views about the proper extent of AI-enabled automation in healthcare work, and which clinical tasks should or should not be automated. We identified a cluster of characteristics of tasks that were considered more suitable for automation. The second involved expectations about the impact of AI on clinical skills, and whether AI-enabled automation would lead to worse or better quality of healthcare. The third tension implicitly contrasted two models of healthcare work: a human-centric model and a technology-centric model. These models assumed different values and priorities for healthcare work and its relationship to AI-enabled automation. CONCLUSION: Our study shows that a diverse group of professional stakeholders involved in healthcare AI development, acquisition, deployment and regulation are attentive to the potential impact of healthcare AI on clinical skills, but have different views about the nature and valence (positive or negative) of this impact. Detailed engagement with different types of professional stakeholders allowed us to identify relevant concepts and values that could guide decisions about AI algorithm development and deployment.
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“This is consistent with Aquino et al., (2023) [25] views, who argue for a synergistic approach where AI complements but does not replace human judgment.”
From Leading with AI in critical care nursing: challenges, opportunities, and the human factor (2024) · cites this paper as Aquino et al., (2023) [25]
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Mason, C., Chen, H., & EVANS, D. B. (2024) · Qeios · doi:10.32388/lavwiy.2 · Cited by 2
Harcus, J., Ferrari, G., Berry, E. B., Cadogan, E., McNally, C. S., Bardwell, A., Singh, N., & Beck, J. J. W. (2024) · Radiography · doi:10.1016/j.radi.2024.10.017 · Cited by 1
Conditional Time Series Modeling for Pneumoconiosis Progression Risk Prediction with Missing Data
Ren, X., Zhao, Z., Jia, L., Zhao, J., Jia, B., Qiang, Y., Zhao, H., & Yue, H. (2025) · Cognitive Computation · doi:10.1007/s12559-025-10417-1 · Cited by 1
Are we sleepwalking into a fully automated medical imaging service?
Gale, N. (2025) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2025.101969 · Cited by 1
Palmer, A., & Schwan, D. (2025) · The International Library of Bioethics · doi:10.1007/978-3-031-94690-5_4 · Cited by 1
Artificial Intelligence (AI) in Patient–Healthcare Relationships: Psychosocial Perspective
Saikia, M. J., & Borthakur, D. (2025) · IEEE Access · doi:10.1109/access.2025.3611818 · Cited by 2
“Proponents of the dystopian view also see the loss of even the smallest of tasks as limiting learning opportunities to build professionals’ practice [51].”
InfluentialNilsén, P., Svedberg, P., Larsson, I., Petersson, L., Nygren, J., Steerling, E., & Neher, M. (2025) · JMIR Formative Research · doi:10.2196/77843 · Cited by 3
“There is also a debate that many traditional tasks will increasingly be taken over by AI, with the result that radiology staff (and health care staff in general) will need to spend less time on routine tasks, which may result in a decline in some clinical skills [54].”
Ethical considerations in artificial intelligence-powered patient counseling
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Ethical Considerations for Artificial Intelligence Tools in Small Animal Veterinary Medicine
Coghlan, S., & Quinn, T. (2026) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2026.03.001 · Cited by 1
How can we prevent clinical deskilling when using AI?
Scott, I., van der Vegt, A. H., & Douglas, P. (2026) · BMJ Quality & Safety · doi:10.1136/bmjqs-2026-020130 · Cited by 1
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“The impact and possible disruption of AI on the workforce have been raised in both healthcare [25, 26], and embryology specifically [12, 27].”
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“…engaging, complex, and enjoyable tasks, enhancing task significance (Figure 3; Al Harbi et al., 2024; Alkaabi & Elsori, 2025; Amann et al., 2023; Aquino et al., 2023; Banerjee et al., 2021; Buck et al., 2022; Chen et al., 2021; Durukan et al., 2026; Drogt et al., 2024; Fruehwirt & Duckworth,…”
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Navigating the impact of artificial intelligence on our healthcare workforce.
Lagadec, D. L., Kornhaber, R., & Cleary, M. (2024) · Journal of Clinical Nursing · doi:10.1111/jocn.17191 · Cited by 1
AI Adoption and Firm Demand for Workers and Skills: Insights from Online Job PostingsPreprint
Mason, C., Chen, H., & EVANS, D. B. (2024) · Qeios · doi:10.32388/lavwiy · Cited by 1
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“Finally, excessive dependence on AI may promote deskilling [28], limiting both the maintenance and, in some cases, the development of professional expertise [29].”
Rigley, E., Krook, J., & Bentley, C. (2023) · International Conference on Transdisciplinary AI (TransAI) · doi:10.1109/transai60598.2023.00044
“Consequently, some scholars propose adopting a transdisci-plinary approach which involves collaboration between technicians and domain experts, such as healthcare professionals (see [24]).”
Metadata
- Authors
- Aquino, Y. S. J., Rogers, W. A., Braunack-Mayer, A., Frazer, H., Win, K. T., Houssami, N., Degeling, C., Semsarian, C., & Carter, S. M.
- Affiliations
- University of Wollongong (AU)
- Macquarie University (AU)
- St Vincent's Hospital Melbourne (AU)
- The University of Sydney (AU)
- Corresponding author
- Aquino, Yves Saint James
- ORCID
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 169
- Pages
- 104903
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W4307902875
- Cited by
- 131
- References
- 36
- Citations by year
- 2026: 36 · 2025: 44 · 2024: 37 · 2023: 14
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion)
- The Sydney eScholarship Repository (The University of Sydney) (submittedVersion, cc-by-nc)
- Funders
- National Health and Medical Research Council (1181960)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- artificial intelligence in healthcare, deskilling, clinical skills, clinical roles, clinical tasks, quality of care, professional stakeholders, AI regulation
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Humans
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