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How Do Radiologists Currently Monitor AI in Radiology and What Challenges Do They Face? An Interview Study and

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This summary is machine-generated.

Radiologists face challenges monitoring artificial intelligence (AI) in radiology due to nascent practices and resource limitations. A mixed-methods approach and multidisciplinary governance are recommended for effective AI oversight.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) integration in radiology is rapidly increasing.
  • Current methods for monitoring AI performance degradation are unclear.
  • Effective AI monitoring is crucial for patient safety and diagnostic accuracy.

Purpose of the Study:

  • To investigate current practices for monitoring AI in radiology.
  • To identify challenges faced by radiologists in AI monitoring.
  • To propose recommendations for robust AI monitoring strategies.

Main Methods:

  • Semi-structured virtual interviews with 16 radiologists (6 USA, 10 Europe).
  • Automatic transcription and thematic analysis of interview data.
  • Exploration of current AI monitoring techniques and associated difficulties.

Main Results:

  • AI monitoring in radiology is nascent; most projects are not fully deployed.
  • Manual retrospective comparison of AI results to radiology reports is the most common method.
  • Lack of resources and uncertainty in creating scalable monitoring processes are key challenges.

Conclusions:

  • Current AI monitoring practices are underdeveloped, with manual methods prevailing.
  • Robust, scalable AI monitoring requires dedicated resources and clear guidelines.
  • A mixed-methods approach and multidisciplinary AI governance groups are recommended for effective oversight.