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Area of Science:

  • Medical Education
  • Artificial Intelligence
  • Radiology

Background:

  • Generative artificial intelligence (AI) chatbots, such as ChatGPT, are advancing rapidly.
  • These AI tools show promise for applications in medical education.

Purpose of the Study:

  • To evaluate the efficacy of ChatGPT in creating board-style multiple-choice questions (MCQs) for radiology resident education.
  • To compare AI-generated MCQs with those developed by attending radiologists.

Main Methods:

  • ChatGPT generated MCQs from resident lecture transcripts using a custom prompt.
  • A mixed set of 17 ChatGPT-generated and 11 attending radiologist-written MCQs were used.
  • Radiology residents (n=21) answered MCQs, rated their educational effectiveness, and identified the perceived author.

Main Results:

  • No significant difference in perceived MCQ quality between ChatGPT-generated (M=6.93) and attending radiologist-written MCQs (M=7.08).
  • Similar MCQ correct answer percentages for ChatGPT-generated (57%) and attending radiologist-written (59%) questions.
  • A lower percentage of ChatGPT-generated MCQs were identified as written by an attending radiologist (57%) compared to those actually written by attendings (71%).

Conclusions:

  • Large language models like ChatGPT show potential for generating educational content in radiology.
  • Further exploration of AI tools for radiology education is warranted.
  • AI-generated MCQs can serve as a valuable supplement to traditional educational methods.