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What Is Implementation Science: And Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application

Ahmad Fayaz-Bakhsh1, Janice Tania2, Syaheerah Lebai Lutfi3

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Summary

Implementation science (IS) is crucial for adopting artificial intelligence (AI) in medical imaging. IS provides strategies to overcome barriers, ensuring AI tools are effectively integrated into clinical practice for better patient outcomes.

Keywords:
Artificial intelligenceEvidence-based practiceImplementation outcomeImplementation scienceIntegrated knowledge translationKnow-do gapMedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Implementation Science

Background:

  • Rapid advancements in artificial intelligence (AI) for medical imaging have outpaced clinical adoption.
  • A significant gap persists between AI development and its integration into routine healthcare settings.
  • Organizational, behavioral, and infrastructural challenges hinder the real-world implementation of AI in medicine.

Purpose of the Study:

  • To highlight the essential role of implementation science (IS) in bridging the gap between AI innovation and clinical practice.
  • To outline how IS frameworks and strategies can address barriers to AI adoption in medical imaging.
  • To advocate for collaborative approaches to ensure the successful integration of AI tools in healthcare.

Main Methods:

  • The article reviews existing literature and frameworks within implementation science.
  • It emphasizes the application of IS principles, including hybrid designs and human-centered approaches.
  • It advocates for stakeholder engagement and multisectoral partnerships in the co-creation process.

Main Results:

  • Implementation science offers structured methodologies to overcome adoption barriers for AI in medical imaging.
  • Collaborative, stakeholder-driven approaches are vital for developing usable and sustainable AI solutions.
  • Early engagement and partnerships facilitate the translation of AI innovations into clinical practice.

Conclusions:

  • Integrating implementation science is essential for the successful translation of AI in medical imaging into routine clinical use.
  • Addressing organizational, behavioral, and infrastructural challenges through IS is key to maximizing AI's impact.
  • Co-creation and partnerships are fundamental to ensuring AI tools are effective, sustainable, and clinically impactful.