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May 10, 2026npj Digital MedicineOpen Access

Framework for artificial intelligence implementation research in healthcare: synthesizing current evidence on barriers and facilitators

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Authors

MPMelanie PowisPrincess Margaret Cancer CentreALAly M. LadakUniversity of TorontoALA. LakeyPrincess Margaret Cancer Centre

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Implication

Scoping literature review synthesizes barriers and facilitators of AI adoption in healthcare, guiding future implementation.

Key Points

  • This research aims to synthesize existing knowledge on barriers to artificial intelligence implementation in healthcare settings.
  • Scoping literature review of articles published from 01/2020 to 05/2024.
  • Use of snowball sampling to identify additional relevant frameworks.
  • Affinity diagramming employed to consolidate constructs and domains across theories.
  • Developed the FAIIR-H framework comprising 12 domains and 63 constructs across five themes.
  • Identified key themes: Design and Development, Organization and Culture, Deployment and Maintenance, with Quality and Safety, and Equity as overarching themes.
  • The framework aids in creating tailored implementation plans for AI in healthcare.

Cite This Study

Powis et al. (2026) studied this question.

synapsesocial.com/papers/6a0020aec8f74e3340f9b8a5https://doi.org/10.1038/s41746-026-02705-3
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Economic, ethical, and regulatory dimensions of artificial intelligence in healthcare: an integrative review2025 · 45 citations
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  4. 4Comprehensive recommendations for the implementation of artificial intelligence in healthcare: a narrative review on facilitators and barriers2026 · 2 citations
  5. 5A practical framework for operationalising responsible and equitable artificial intelligence in health care: tackling bias, inequity, and implementation challenges2026 · 12 citations