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Cited by 17Artificial Intelligence in Healthcare and EducationArtificial Intelligence ApplicationsHealth Policy Implementation Science

Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts

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Leenen, Jobbe P.L., Hiemstra, Paul, Ten Hoeve, Martine M., Jansen, Anouk C.J., van Dijk, Joris D., Vendel, Brian, Versteeg, Guido, Hakvoort, Gido A. +1 more·2025·PLOS Digital Health
DOI 10.1371/journal.pdig.0000847PMC PMC12057897
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Abstract

Artificial Intelligence (AI)-based tools have shown potential to optimize clinical workflows, enhance patient quality and safety, and facilitate personalized treatment. However, transitioning viable AI solutions to clinical implementation remains limited. To understand the challenges of bringing AI into clinical practice, we explored the experiences of healthcare professionals, researchers, and Policy and Governance Experts in hospitals. We conducted a qualitative study with thirteen semi-structured interviews (mean duration 52.1 ± 5.4 minutes) with healthcare professionals, researchers, and Policy and Governance Experts, with prior experience on AI development in hospitals. The interview guide was based on value, application, technology, governance, and ethics from the Innovation Funnel for Valuable AI in Healthcare, and the discussions were analyzed through thematic analysis. Six themes emerged: (1) demand-pull vs. tech-push: AI development focusing on innovative technologies may face limited success in large-scale clinical implementation. (2) Focus on generating knowledge, not solutions: Current AI initiatives often generate knowledge without a clear path for implementing AI models once proof-of-concept is achieved. (3) Lack of multidisciplinary collaboration: Successful AI initiatives require diverse stakeholder involvement, often hindered by late involvement and challenging communication. (4) Lack of appropriate skills: Stakeholders, including IT departments and healthcare professionals, often lack the required skills and knowledge for effective AI integration in clinical workflows. (5) The role of the hospital: Hospitals need a clear vision for integrating AI, including meeting preconditions in infrastructure and expertise. (6) Evolving laws and regulations: New regulations can hinder AI development due to unclear implications but also enforce standardization, emphasizing quality and safety in healthcare. In conclusion, this study highlights the complexity of AI implementation in clinical settings. Multidisciplinary collaboration is essential and requires facilitation. Balancing divergent perspectives is crucial for successful AI implementation. Hospitals need to assess their readiness for AI, develop clear strategies, standardize development processes, and foster better collaboration among stakeholders.

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Cited by

Sorted by citations

Authors
Leenen, J. P. L., Hiemstra, P., Ten Hoeve, M. M., Jansen, A. C. J., van Dijk, J. D., Vendel, B., Versteeg, G., Hakvoort, G. A., & Hettinga, M.
Affiliations
  • Windesheim University of Applied Sciences (NL)
  • Isala (NL)
Corresponding author
Leenen, Jobbe P.L.
ORCID
Venue
PLOS Digital Health
Publisher
Public Library of Science (PLoS)
Published
2025-05-07
Volume
4
Issue
5
Pages
e0000847
ISSN
2767-3170 (ISSN-L)
DOI
10.1371/journal.pdig.0000847
PMC
PMC12057897
OpenAlex
W4410153852
Cited by
17
References
7
Citations by year
2026: 13 · 2025: 4
FWCI
4.09
Type
journal article
Language
en
Open access
Licence
Locations
  • PLOS Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
TechForFuture
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
Keywords
AI implementation, clinical practice, healthcare workers, hospital governance, multidisciplinary collaboration, stakeholder engagement, AI skills gap, AI regulation, clinical workflow, AI ethics and governance

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