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A holistic approach to implementing artificial intelligence in radiology.

Bomi Kim1, Stephan Romeijn2, Mark van Buchem3

  • 1House of Innovation (Department of Entrepreneurship, Innovation and Technology), Stockholm School of Economics, Stockholm, Sweden.

Insights Into Imaging
|January 25, 2024
PubMed
Summary

Implementing artificial intelligence (AI) in healthcare requires a holistic strategy. This case study shows aligning technology, workflow, and people is key for successful AI adoption in radiology.

Keywords:
Artificial intelligenceChange managementDigital technologyImplementation scienceInformation systems

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Area of Science:

  • Medical Informatics
  • Radiology
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) adoption is critical for modern healthcare, yet implementation faces significant barriers.
  • Limited practical insights exist for successfully integrating AI into clinical practice, particularly in radiology.

Purpose of the Study:

  • To address the AI implementation gap in healthcare by presenting findings from a detailed case study.
  • To explore a holistic approach to AI implementation within a radiology department.

Main Methods:

  • A three-year longitudinal, qualitative case study was conducted at a Dutch academic medical centre.
  • Data collection included work and meeting observations, interviews, and document analysis.
  • Abductive reasoning was employed for systematic data analysis, identifying key change initiative themes.

Main Results:

  • Identified multi-level challenges in AI implementation: technological (interoperability), workflow (limited interaction), and socio-organizational (divergent expectations, limited experience).
  • A holistic approach, aligning social and technological aspects, enables organizations to maximize AI benefits.
  • The case study illustrates that long-term initiatives are crucial for successful AI integration.

Conclusions:

  • A holistic strategy addressing technology, workflow, and organizational factors is essential for AI implementation in clinical settings.
  • Aligning change initiatives across these levels facilitates the widespread adoption of AI in radiology.
  • This approach helps organizations create sustainable value through AI integration.