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Related Experiment Videos

Bridging the Gap: Barriers and Adoption Patterns of AI in Radiological Practice.

Lior Moskovich1,2, Mor Saban2, Nitza Geri1

  • 1Department of Management and Economics, The Open University of Israel, Israel.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary

Artificial Intelligence (AI) adoption in radiology is inconsistent. Key barriers include trust, workflow, and medico-legal concerns, highlighting the need for robust governance and institutional support for successful AI integration.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial Intelligence (AI) is rapidly advancing in radiological practice.
  • Real-world adoption of AI tools in radiology faces challenges and remains unstable.
  • Understanding the current state of AI integration within the radiological workforce is crucial.

Purpose of the Study:

  • To examine the current use and adoption patterns of AI in the Israeli radiological workforce.
  • To identify perceptions, attitudes, and barriers related to AI implementation among radiologists.
  • To explore factors influencing the sustainable integration of AI in clinical radiology.

Main Methods:

  • A mixed-methods approach was employed.
  • Data collected through 32 semi-structured interviews with radiological professionals.
Keywords:
AI adoptionArtificial Intelligencebarriersradiology

Related Experiment Videos

  • A survey was administered to 133 radiology professionals.

    • Main Results:

    • AI is primarily viewed as a supportive 'second reader' tool.
    • Radiologists expressed concerns regarding AI reliability, workflow integration, and medico-legal responsibilities.
    • Adoption varied, with higher usage reported by specialists and hospital-based professionals.

    Conclusions:

    • Conditional acceptance of AI is prevalent, contingent on physician responsibility and human oversight.
    • Significant barriers include budget limitations, doubts about AI output accuracy, and lack of medico-legal frameworks.
    • Trust, clear governance structures, and institutional support are essential for sustainable AI adoption in radiology.