利用ChatGPT来增强放射学入住者教育中的学习
Aaron Zheng1, Cole J Barker1, Sergio S Ferrante1
1Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213.
Academic radiology
|July 8, 2025
概括
像ChatGPT这样的人工智能聊天机器人可以生成有效的放射学板式练习问题. 这些人工智能生成的问题在质量和准确性方面与参与放射科医生撰写的问题相美.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 生成型人工智能 (AI) 聊天机器人,如ChatGPT,正在迅速发展.
- 这些人工智能工具对医学教育中的应用非常有希望.
研究的目的:
- 评估ChatGPT在为放射科住院教育创建板式多选择题 (MCQ) 的有效性.
- 将人工智能生成的MCQ与参与放射科医生开发的MCQ进行比较.
主要方法:
- 通过使用自定义提示符,ChatGPT从居民讲座成绩单中生成MCQ.
- 使用了一组混合的17个ChatGPT生成的MCQ和11个参加放射科医生撰写的MCQ.
- 放射科住院生 (n=21) 回答了MCQ,评价了他们的教育有效性,并确定了被认为的作者.
主要成果:
- 在ChatGPT生成的MCQ (M=6.93) 和参加放射科医生撰写的MCQ (M=7.08) 之间,感知到的MCQ质量没有显著差异.
- 类似的MCQ正确答案百分比用于ChatGPT生成 (57%) 和参加放射科医生撰写 (59%) 的问题.
- 与实际上由护理人员 (71%) 撰写的MCQ相比,较少的ChatGPT生成MCQ被认定为由主治放射科医生 (57%) 撰写的.
结论:
- 像ChatGPT这样的大型语言模型显示出在放射学中生成教育内容的潜力.
- 为放射学教育进一步探索人工智能工具是有必要的.
- 人工智能生成的MCQ可以作为传统教育方法的宝贵补充.
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