评估和验证健康教育的大型语言模型关于部发育性形:两阶段研究与专家评级和试点随机对照试验
Hui Ouyang1,2, Gan Lin1,2, Yiyuan Li1,2
1Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Journal of medical Internet research
|January 19, 2026
概括
大型语言模型 (LLM) 在产生部发育性形 (DDH) 教育材料方面表现出不同的能力. 士辅助教育适度提高了护理人员的电子健康素养和DDH知识,但不能取代临床判断.
科学领域:
- 儿科整形外科 儿科整形外科
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 部发育性形 (DDH) 是一种常见的儿科骨科疾病,需要有效的健康教育来管理.
- 大型语言模型 (LLM) 为健康教育提供了新的机会,但它们在DDH环境中的实用性仍未得到评估.
研究的目的:
- 为DDH评估LLM生成的教育材料的质量和教育有效性.
主要方法:
- 一项两阶段研究评估了4名LLM,使用专家评价和对127名护理人员进行随机对照试验 (RCT).
- 评估了LLM答案的准确性,流利性,丰富性和可理解性;RCT比较了LLM辅助教育与网络搜索电子健康素养和DDH知识.
主要成果:
- 在LLM产生的内容质量方面发现了显著的差异. 与对照组相比,LLM辅助组表现出更好的电子健康素养和DDH知识.
- 像ChatGPT-4和DeepSeek-V3这样的特定LLM产生了更准确的内容,而Gemini 2.0 Flash则更流利.
结论:
- 主流的LLM在产生DDH教育内容方面具有不同的能力,在护理人员的电子健康素养和知识方面提供了适度的改进.
- 法学士可以补充,但不能取代临床评估;未来的工作应该通过简单的语言和个性化来增强法学士的成果.
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