探索放射学研究生与教育目的的大型语言模型的接触:对知识,态度和实践的研究
Pradosh Kumar Sarangi1, Braja Behari Panda2, Sanjay P3
1Department of Radiodiagnosis, All India Institute of Medical Sciences, Deoghar, Jharkhand, India.
The Indian journal of radiology & imaging
|December 19, 2024
概括
放射学研究生熟悉大型语言模型 (LLMs),并看到它们的教育益处. 尽管积极的态度,但对局限性的担忧导致在放射学培训中谨慎使用.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 放射学培训 放射学培训
背景情况:
- 大型语言模型 (LLM) 越来越多地被探索以加强医学教育.
- 关于放射学研究生与LLMs的接触,人们的理解有限.
- 这项研究调查了他们关于教育中的LLM的知识,态度和做法.
研究的目的:
- 评估放射学研究生对LLMs的熟悉程度.
- 评估他们对将LLMs纳入医学教育的态度.
- 了解他们利用LLMs学习的现行实践.
主要方法:
- 在线进行了一项横截面的定量调查.
- 参与者在全国范围内使用社交媒体和雪球采样招募.
- 一个经过验证的问卷评估了关于LLMs的知识,态度和实践.
主要成果:
- 252名放射学研究生参与了调查,其中47.62%的人熟悉LLMs.
- 大多数 (71.82%) 认为将LLMs与传统工具相结合有潜力.
- 学生表现出积极的态度 (平均得分3.75),但由于局限性,他们的实践水平较低 (平均得分3.15).
结论:
- 放射学研究生对LLM有所了解,并承认其潜在的教育价值.
- 积极的态度存在,但实际应用受到LLM限制的担忧所限制.
- 进一步的整合策略应解决这些问题,以优化LLM在放射学教育中的使用.
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