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大型语言模型在手术前和放电教育中的有效性:基于评估框架的系统审查

Mengting Wang¹, Haoming Ma², Meihua Piao³

¹School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.

NPJ digital medicine

|January 7, 2026

概括

大型语言模型 (LLM) 在减少患者焦虑方面表现有希望,但缺乏改善疼痛或恢复的一致证据. 需要进一步的研究来评估它们的临床有效性和可信度.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能
患者教育患者教育

背景情况:

大型语言模型 (LLM) 越来越多地用于患者教育.
目前对LLM在医疗保健中的有效性的评估是不一致的.
了解LLM对患者结果的影响对于临床整合至关重要.

研究的目的:

系统地审查在手术前和退院教育中基于LLM的干预措施的有效性.
确定评估LLM特征及其对患者结果的影响的证据差距.

主要方法:

来自五个数据库的20项研究的系统审查 (开始至2025年4月18日).
使用四维框架对结果和干预评估进行叙述综合.
对干预特征和结果的报告模式的热图可视化.

主要成果:

通过LLM干预,患者的焦虑和一些满意度方面都有所改善.
与传统方法相比,没有发现疼痛,恢复或其他满意度指标的显著差异.
与临床终点相对应的LLM可信度和绩效的报告有限.

结论:

基于LLM的患者教育显示出潜在的特定益处,例如减少焦虑.
在评估LLM绩效,可信度和临床影响方面存在重大差距.
未来的研究应该整合以模型为中心和以患者为中心的评估,以实现负责任的部署.

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Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.