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使用大型语言模型人工智能增强临床能力委员会洞察力

Mark A Pittman^1,2,3, Carl Ehrett⁴, Mirinda A Gormley^1,2,3

¹Department of Emergency Medicine Prisma Health Upstate Greenville South Carolina USA.

AEM education and training

|February 25, 2026

概括

人工智能 (AI) 在评估居民反方面表现有前途,产生比人类评估更高的质量和准确性的评估. 虽然人工智能产生的反是可比或优越的,但它的有用性需要进一步研究.

科学领域:

医学教育医学教育
医疗保健中的人工智能
临床能力评估临床能力评估

背景情况:

大型语言模型 (LLM) 提供了增强居民评估的潜力.
有限的研究存在,将AI与临床评估的人类分析进行比较.
本研究评估了LLM AI用于居民反分析.

研究的目的:

评估LLM AI在评估居民反方面的有效性.
将人工智能产生的居民评估与人类产生的评估进行比较.
探索人工智能在简化教师审查过程中的作用.

主要方法:

利用LLM Llama-3.1 70B来分析紧急医疗 (EM) 教育者对31名EM居民的反.
人工智能生成了居民优势,弱点和里程碑性能的总结.
临床能力委员会 (CCC) 成员对人工智能产生的内容质量,准确性,特异性和有用性进行了调查.

主要成果:

人工智能生成的评估比人类评估 (79个字) 长 (391个字).
在质量,准确性和特异性方面,CCC成员对AI内容进行了更有利的评价.
实用性评级偏爱人类生成的内容,尽管人工智能被评为54.1%的可接受性.

结论:

人工智能可以产生EM居民评估,其质量,准确性和特异性与人类评估相当或优于人类评估.
人工智能驱动的评估有可能简化教育工作者审查并减少工作量.
对人工智能产生的反有用性的进一步调查是有必要的.

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