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检索增强的ChatGPT-4o提高了准确性,但降低了关节镜患者教育中的可读性

Onur Gültekin¹, Erdem Aras Sezgin², Oğuzhan Cakır¹

¹Department of Orthopaedics and Traumatology, University of Health Sciences, Fatih Sultan Mehmet Training and Research Hospital, Istanbul, Türkiye.

Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA

|December 1, 2025

概括

与标准模型相比,ChatGPT-4o的"深度研究"模式为关节透视信息提供了更高的准确性和全面性. 然而,标准模型提供了更好的可读性,建议采取混合方法,以获得最佳的患者教育.

科学领域:

整形外科手术整形外科手术
人工智能在医学中的应用
医疗信息学医疗信息学

背景情况:

大型语言模型 (LLM) 对患者教育有希望,但它们在骨科等专业医疗领域的可靠性是不确定的.
评估不同的LLM配置对于了解它们在提供准确患者信息方面的潜力和局限性至关重要.

研究的目的:

为了比较标准ChatGPT-4o及其"深度研究"模式的响应的准确性,可读性和以患者为中心.
评估这些人工智能模型对关节镜患者教育的适用性.

主要方法:

使用了30个关于关节镜检查的标准化患者问题.
标准的ChatGPT-4o及其"深度研究"模式的响应被两个骨科外科医生评估.
评估包括准确性,清晰性,全面性和可读性,使用利克特尺度和弗莱什-金凯德指标.

主要成果:

"深度研究"模式显示了显著更高的准确性 (p=0.012) 和全面性 (p<0.001).
标准聊天GPT-4o显示出优异的清晰度 (p=0.048) 和可读性得分 (弗莱什-金凯德分级和阅读易度得分;p<0.001).
可读性利克特分数在模型之间是可比的 (p=0.729).

结论:

"深度研究"模式提供了更大的科学严谨性,而标准模型在关节镜患者教育的可读性方面表现出色.
混合方法可能会提高教育效率,但临床监督对于防止错误信息至关重要.
调查结果表明存在微小的差异,并突出了LLM的准确性-可读性权衡,这需要进一步的探索性研究.

关键词:

聊天GPT 聊天在GPT 聊天人工智能的人工智能是人工智能.关节关节镜检查关节镜检查大型语言模型.患者教育患者教育检索-增强生成

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