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大型语言模型在放射学实习申请中的使用:不受欢迎但不可避免

Emile B Gordon¹, Charles M Maxfield², Robert French²

¹Department of Radiology, Duke University Health System, Durham, North Carolina; Department of Radiology, University of California San Diego, La Jolla, California.

Journal of the American College of Radiology : JACR

|September 19, 2024

概括

放射学项目主任发现大型语言模型 (LLM) 产生的个人陈述质量较低. 尽管对真实性的担忧,他们认识到人工智能在居住申请中的使用越来越多.

科学领域:

医学教育医学教育
医疗保健中的人工智能
放射学住院招生放射学住院招生

背景情况:

包括大型语言模型 (LLM) 在内的人工智能 (AI) 的使用在各种专业领域正在迅速增加.
医学,特别是放射学等竞争性领域的住院申请在很大程度上依赖个人陈述来评估申请人的适合性.
人们越来越担心人工智能工具可能产生的或增强的个人陈述的真实性和质量.

研究的目的:

调查放射学课程主任对LLM产生的个人陈述由居住申请人提交的影响的观点.
评估人工智能生成的个人陈述在质量和真实性方面与人类撰写的陈述相比是如何被认为的.

主要方法:

这是一项混合方法研究,涉及八名放射学项目主任.
参与者完成了一项调查,并在审查匿名个人陈述 (原始和使用GPT-4生成的AI) 后参与焦点小组讨论.
声明被评估在写作质量 (声音,清晰度,参与度,组织) 和感知来源使用5分利克尔特尺度.

主要成果:

人工智能生成的陈述的质量评级较低 (平均为56%或更差),而不是人类撰写的陈述 (平均为29%或更差).
审查人员可靠地识别了由人类撰写的陈述 (95%),尽管对区分人工智能生成的内容的信心很低.
焦点小组透露了对人工智能降低真实性和个人陈述价值的担忧,对人工智能监管的意见分歧.

结论:

放射学课程主任认为LLM产生的个人陈述质量较差,并注意到申请人独特的声音的潜在损失.
虽然董事们可以可靠地区分人工智能产生的和人类撰写的陈述,但他们承认人工智能在应用材料中的使用越来越普遍和不可避免.
这些发现强调需要进一步讨论人工智能在医疗住院招生中的伦理使用和监管.

关键词:

聊天GPT 聊天在GPT 聊天法学士 (LLM) 是一个专业.教育教育教育教育的教育.医学院的学生医学院的学生居住地居住地

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