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Related Experiment Video

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A multinational study on artificial intelligence adoption: Clinical implementers' perspectives.

Luis Marco-Ruiz1, Miguel Ángel Tejedor Hernández1, Phuong Dinh Ngo1

  • 1Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway.

International Journal of Medical Informatics
|February 20, 2024
PubMed
Summary
This summary is machine-generated.

Implementing artificial intelligence (AI) in healthcare requires more than just research; it needs clinical validation, practitioner involvement, and better data management. Addressing these factors is key to successful AI adoption in clinical settings.

Keywords:
AI implementationArtificial intelligenceHealthcareImplementation scienceMachine learningeHealth

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Area of Science:

  • Healthcare AI
  • Clinical Implementation
  • Medical Informatics

Background:

  • Despite AI advancements in healthcare, many systems fail to transition from research to clinical practice.
  • AI investments often stall at the prototype stage, hindering real-world application.

Purpose of the Study:

  • To identify challenges and success factors for clinical AI implementation.
  • To gather insights from AI implementers to improve future adoption rates.

Main Methods:

  • Qualitative analysis of semi-structured interviews with 37 clinical AI implementers.
  • Framework method used to analyze challenges, success factors, and proposals for AI adoption.

Main Results:

  • Clinical validation is prioritized over AI explainability.
  • Involvement of clinical practitioners and improved data management structures are crucial.
  • Enhanced regulatory support, increased AI literacy, and better funding schemes are needed for AI implementation.

Conclusions:

  • AI holds promise for clinical settings, but successful transfer requires regulatory, procedural, educational, and financial adjustments.
  • Implementing AI effectively necessitates a multi-faceted approach addressing validation, stakeholder engagement, and infrastructure.