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Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.

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Implementing artificial intelligence (AI) in medical imaging requires understanding both facilitators and barriers. A systematic review found a need for more holistic, human-centric approaches beyond just technological performance.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Clinical Workflow Integration

Background:

  • Artificial intelligence (AI) is increasingly adopted in healthcare, especially medical imaging, promising efficiency gains.
  • However, systematic evidence on process factors for successful AI implementation into clinical workflows is limited.

Purpose of the Study:

  • To systematically assess and synthesize facilitators and barriers to AI implementation in routine medical imaging.
  • To explore relationships between these factors across different implementation stages.

Main Methods:

  • Systematic review of 6 medical databases, including 38 original studies.
  • Qualitative content analysis to extract facilitators, barriers, outcomes, and moderators.
  • Epistemic network analysis to explore relationships across AI implementation stages.

Main Results:

  • Identified 12 key dimensions and 37 subthemes influencing AI implementation.
  • 20 themes acted as both facilitators and barriers.
  • Studies predominantly focused on performance metrics, often overlooking clinician experiences and outcomes.

Conclusions:

  • Successful AI implementation in medical imaging requires a thorough understanding of facilitators and barriers.
  • Highlights the utility of AI in clinical care and workflow integration.
  • Emphasizes the need for comprehensive reporting and a human-centric approach, moving beyond purely technological considerations.