Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills
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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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A synergistic approach where AI complements but does not replace human judgment is argued for by Aquino et al. (2023).
“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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“Finally, excessive dependence on AI may promote deskilling [28], limiting both the maintenance and, in some cases, the development of professional expertise [29].”
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- 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
- Social Sciences › Safety Research › 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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