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A "Bumper-Car" Curriculum for Teaching Deep Learning to Radiology Residents☆.

Michael L Richardson1, Patricia I Ojeda2

  • 1Department of Radiology, University of Washington, Seattle, Washington.

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This summary is machine-generated.

Residents learned to create, train, and evaluate deep learning (DL) models using a hands-on curriculum. This AI training improved their understanding and interest in DL applications for radiology.

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Area of Science:

  • Medical education
  • Artificial intelligence in radiology
  • Deep learning model development

Background:

  • Artificial intelligence (AI) and deep learning (DL) are increasingly important in medical imaging.
  • Radiology residents require training to effectively utilize and critically assess AI tools.
  • Existing curricula often focus on theory rather than practical application.

Purpose of the Study:

  • To develop and evaluate a hands-on AI training curriculum for radiology residents.
  • To teach residents the practical skills of creating, training, evaluating, and refining DL models.
  • To foster resident confidence and interest in applying AI in their future practice.

Main Methods:

  • A 6-hour, three-session curriculum was designed using the no-code platform Lobe.ai.
  • Residents engaged in pre-class assignments including data preparation and labeling.
  • Training sessions emphasized hands-on model development with minimal focus on complex mathematics or programming.

Main Results:

  • Residents successfully acquired and labeled diverse image datasets.
  • Participants demonstrated proficiency in training, evaluating, and refining DL models.
  • Survey results indicated high resident satisfaction, increased interest in AI, and a perceived value of the practical skills learned.

Conclusions:

  • The hands-on curriculum effectively met its objectives in training residents in DL model development.
  • The practical experience gained is expected to empower residents to identify and address issues in diagnostic AI systems.
  • The course is recommended for residents, medical students, and faculty, highlighting its broad applicability.