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Using Generative Artificial Intelligence When Writing Letters of Recommendation.

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    Generative AI (genAI) can help academic medicine faculty write better letters of recommendation (LORs) more efficiently. This framework guides the ethical use of genAI for LORs, ensuring quality and privacy.

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    Area of Science:

    • Academic Medicine
    • Artificial Intelligence
    • Medical Education

    Background:

    • Writing letters of recommendation (LORs) is a crucial but time-consuming faculty responsibility in academic medicine.
    • Current methods for LORs can be repetitive and may sacrifice quality due to time constraints.
    • Pass/fail grading and remote interviews increase the importance of well-crafted LORs.

    Purpose of the Study:

    • To provide a framework for the ethical and effective use of generative artificial intelligence (genAI) in writing LORs for academic medicine.
    • To guide faculty in leveraging genAI tools to improve the quality and efficiency of LOR creation.
    • To address privacy concerns and promote responsible AI implementation in medical education.

    Main Methods:

    • Discussing the fundamental structure and advantages of various genAI models for letter writing.
    • Outlining iterative methods for developing effective prompts to generate LOR drafts.
    • Detailing strategies for personalizing content, identifying bias, and documenting AI usage.

    Main Results:

    • A practical framework for faculty to integrate genAI into their LOR writing process is presented.
    • The framework aims to enhance LOR quality by avoiding shortcuts like copying and pasting.
    • Ethical considerations, including privacy and disclosure, are central to the proposed approach.

    Conclusions:

    • GenAI offers a valuable opportunity to streamline LOR writing in academic medicine while maintaining high standards.
    • Ethical use, privacy, and transparency are paramount for successful genAI implementation in this context.
    • Further research is needed to develop specialized AI models for generating LORs, but this framework enables immediate adoption.