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    科学领域:

    • 学术医学 学术医学
    • 人工智能的人工智能
    • 医学教育 医学教育

    背景情况:

    • 撰写推信 (LOR) 是学术医学中一个关键但耗时的教师责任.
    • 目前用于LOR的方法可能是重复性的,并且可能因为时间限制而牺牲质量.
    • 通过/失败评分和远程面试增加了精心制作的LOR的重要性.

    研究的目的:

    • 为创造性人工智能 (genAI) 的道德和有效使用提供框架,为学术医学撰写LOR.
    • 引导教师利用genAI工具来提高LOR创作的质量和效率.
    • 解决隐私问题,促进医疗教育中负责任的人工智能实施.

    主要方法:

    • 讨论各种genAI模型的基本结构和优势,以写信.
    • 概述开发有效提示生成LOR草稿的代方法.
    • 详细介绍内容个性化,偏见识别和AI使用记录的策略.

    主要成果:

    • 介绍了教师将genAI整合到他们的LOR写作过程中的实际框架.
    • 该框架旨在通过避免复制和粘贴等快捷方式来提高LOR质量.
    • 伦理考虑,包括隐私和披露,是拟议方法的核心.

    结论:

    • 在保持高标准的同时,GenAI为简化学术医学中的LOR写作提供了一个宝贵的机会.
    • 在这种情况下,伦理使用,隐私和透明度对于成功实施genAI至关重要.
    • 需要进一步的研究来开发专门的AI模型来生成LOR,但这种框架可以立即采用.