Comment on “Evaluating Generative Artificial Intelligence as an Educational Tool for Radiology Resident Report Drafting”

IF 5.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology Pub Date : 2026-10-01 Epub Date: 2026-07-07 DOI:10.1016/j.jacr.2026.06.021

Maajid Mohi Ud Din Malik PhD

引用次数: 0

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对“评价生成式人工智能作为放射科住院医师报告起草教育工具”的评论

Verdone及其同事报告说，gpt - 40可以可靠地识别乳腺成像草稿中的报告错误，并产生住院医生和主治医生认为有用的反馈。研究结果令人鼓舞。然而，在将该工具定位为可扩展的教育辅助工具之前，有三个方法特征值得更仔细的审查：在误差类别的推导中构建循环，Levenshtein过滤器引入的选择偏差，以及gpt - 40加入小组后出席口译员协议的未被强调的下降。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the American College of Radiology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

6.30

自引率

8.90%

发文量

312

审稿时长

34 days

期刊介绍： The official journal of the American College of Radiology, JACR informs its readers of timely, pertinent, and important topics affecting the practice of diagnostic radiologists, interventional radiologists, medical physicists, and radiation oncologists. In so doing, JACR improves their practices and helps optimize their role in the health care system. By providing a forum for informative, well-written articles on health policy, clinical practice, practice management, data science, and education, JACR engages readers in a dialogue that ultimately benefits patient care.