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在风湿病学中残疾和生成AI教育奖学金:教育模块和模拟练习

Sana G Cheema¹, Matthew A Sullivan¹, Emily Balczewski²

¹Washington University in St. Louis School of Medicine, St. Louis, MO.

Arthritis care & research

|December 12, 2025

概括

培训人员 (FIT) 对使用生成AI (genAI) 进行残疾适应信件的信心有所提高. 培训基因AI的增强技能,为风湿和肌肉骨疾病 (RMDs) 患者提供快速工程和信件完成.

科学领域:

医学教育医学教育
类风湿病学类风湿病学
人工智能的人工智能

背景情况:

风湿和肌肉骨疾病 (RMDs) 可以导致影响工作和学校参与的残疾.
生成型人工智能 (genAI) 提供了简化残疾倡导文件的潜力.
在基因AI和残疾倡导方面培训正在培训的学员 (FIT) 对于有效的患者支持至关重要.

研究的目的:

评估在线模块和模拟对FIT在使用genAI进行住宿信件方面的信心和技能的影响.
确定在残疾人倡导的genAI应用程序中进一步指导的领域.

主要方法:

FITs完成了一个在线模块和模拟,重点是工程 genAI 提示住宿信的提示.
技能是使用标签来评估的,信心是通过前后利克特尺度来衡量的.
使用Wilcoxon签名等级测试进行的统计分析比较了训练前和训练后的信心水平.

主要成果:

83%的FIT平均得分很高,即时工程是最强的技能 (95.45%).
最低得分的领域是提供者角色的差异化 (74.55%).
在所有目标中,FIT报告了显著的信心改善,特别是在快速工程和完成genAI起草的信件方面 (p<0.0001).

结论:

教育干预措施有效地教导FIT将genAI集成到写住宿信中.
这项培训提高了FIT解决RMD相关残疾患者差异的能力.
发展这些技能为FIT准备在风湿病学中自信地实践,将genAI整合到残疾人的临床护理中.

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