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Artificial Intelligence Literacy: Developing a Multi-institutional Infrastructure for AI Education.

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This summary is machine-generated.

A new artificial intelligence (AI) in radiology course significantly improved resident knowledge and interest in AI applications. The multi-institutional program addressed AI terminology, clinical uses, and ethical considerations, enhancing radiology education.

Keywords:
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Area of Science:

  • Radiology
  • Medical Education
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) is increasingly integrated into medical imaging.
  • Radiology residents often lack sufficient exposure to AI in their training.
  • There is a need for structured AI education in radiology residency programs.

Purpose of the Study:

  • To evaluate the effectiveness of an AI in radiology literacy course.
  • To assess the impact of the course on participants' knowledge and interest in AI.
  • To provide a foundational understanding of AI in radiology for residents.

Main Methods:

  • A week-long, remote learning course was developed for radiology residents.
  • The curriculum included lectures on AI basics, clinical applications, ethics, and bias.
  • Participants engaged in a hands-on session with an AI-assisted viewer and completed pre- and post-course surveys.

Main Results:

  • The course had an average of 75 daily participants from nine residency programs.
  • Participant knowledge scores significantly increased from 8.3/15 pre-course to 10.1/15 post-course (p=0.04).
  • Nearly all participants (96.7%) reported insufficient prior AI exposure, and 78.6% expressed interest in future AI education.

Conclusions:

  • A multi-institutional AI in radiology literacy course effectively enhanced participants' AI knowledge.
  • The course successfully addressed gaps in radiology resident AI education.
  • A majority of participants showed sustained interest in further AI in radiology education.