Leveraging ChatGPT for Enhancing Learning in Radiology Resident Education.

Publication/Presentation Date

9-1-2025

Abstract

RATIONALE AND OBJECTIVES: Chat generative pre-trained transformer (ChatGPT) is a generative artificial intelligence chatbot based on a LLM at the forefront of technological development with promising applications in medical education. This study aims to evaluate the use of ChatGPT in generating board-style practice questions for radiology resident education.

MATERIALS AND METHODS: Multiple-choice questions (MCQs) were generated by ChatGPT from resident lecture transcripts using a custom prompt. 17 of the ChatGPT-generated MCQs were selected for inclusion in the study and randomly combined with 11 attending radiologist-written MCQs. For each MCQ, the 21 participating radiology residents answered the MCQ, rated the MCQ from 1-10 on effectiveness in reinforcing lecture material, and responded whether they thought an attending radiologist at their institution wrote the MCQ versus an alternative source.

RESULTS: Perceived MCQ quality was not significantly different between ChatGPT-generated (M=6.93, SD=0.29) and attending radiologist-written MCQs (M=7.08, SD=0.51) (p=0.15). MCQ correct answer percentages did not significantly differ between ChatGPT-generated (M=57%, SD=20%) and attending radiologist-written MCQs (M=59%, SD=25%) (p=0.78). The percentage of MCQs thought to be written by an attending radiologist was significantly different between ChatGPT-generated (M=57%, SD=13%) and attending radiologist-written MCQs (M=71%, SD=20%) (p=0.04).

CONCLUSION: LLMs such as ChatGPT demonstrate potential in generating and presenting educational material for radiology education, and their use should be explored further on a larger scale.

Volume

32

Issue

9

First Page

5635

Last Page

5642

ISSN

1878-4046

Disciplines

Medicine and Health Sciences

PubMedID

40628645

Department(s)

Fellows and Residents

Document Type

Article

Link to Full Text

Share

COinS