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Artificial Intelligence and Radiology Education.

Ali S Tejani1, Hesham Elhalawani1, Linda Moy1

  • 1Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390 (A.S.T.); Department of Radiation Oncology, Brigham and Women's Hospital, Boston, Mass (H.E.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology, University of California San Francisco, San Francisco, Calif (M.K.); and Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.).

Radiology. Artificial Intelligence
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Summary
This summary is machine-generated.

Artificial intelligence (AI) in radiology education is crucial for safe clinical implementation. Addressing barriers to AI education ensures radiologists are prepared for AI

Keywords:
Artificial IntelligenceImaging InformaticsImpact of AI on EducationMedical EducationNatural Language ProcessingPrecision EducationUse of AI in Education

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology Education

Background:

  • Clinical implementation of AI necessitates radiologists proficient in AI systems for safe and effective patient care.
  • Growing demand for AI education highlights the transition of AI from research to practice, with trainees showing positive attitudes.
  • Barriers like limited resources hinder adequate preparation for AI in clinical practice.

Purpose of the Study:

  • To outline the current landscape of AI education in radiology.
  • To identify resources and strategies for enhancing AI literacy among radiologists.
  • To explore the potential of AI-augmented and precision education in radiology.

Main Methods:

  • Review of existing AI education initiatives and resources in radiology.
  • Identification of formal courses, self-directed learning materials, and community resources.
  • Discussion of AI-augmented radiology education and precision education concepts.

Main Results:

  • National organizations offer foundational courses (e.g., National Imaging Informatics Course - Radiology, RSNA Imaging AI Certificate).
  • A variety of supplementary resources, including programming courses, video series, and blogs, are available.
  • AI-augmented education, including precision education, offers personalized learning experiences.

Conclusions:

  • Formal and self-directed learning are essential for developing AI-savvy radiologists.
  • Accessible resources and structured curricula are needed to overcome educational barriers.
  • AI holds potential for personalized medical education, adapting to individual trainee needs and clinical exposure.