几个大型语言模型在回答关于儿童1型糖尿病的常见患者问题时的表现:准确性,可理解性和实用性
Yasemin Denkboy Ongen1, Ayla İrem Aydın2, Meryem Atak2
1School of Medicine, Department of Pediatric Endocrinology, Bursa Uludağ University, Gorukle Campus, Bursa, Turkey. ydenkboyongen@uludag.edu.tr.
BMC pediatrics
|October 9, 2025
概括
大型语言模型 (LLM) 在回答儿童1型糖尿病 (T1D) 问题方面表现相似. 像ChatGPT-4o和Gemini Advanced这样的先进模型提供了准确,对患者友好的反应,表明了医疗保健整合的潜力.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理应用程序
- 儿科内分泌学 儿科内分泌学
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地用于患者沟通.
- 儿童1型糖尿病 (T1D) 发病率正在上升,因此需要可靠的信息.
- 与2型糖尿病相比,对儿科T1D调查的LLM性能存在有限的研究.
研究的目的:
- 评估和比较不同LLM在回答常见的1型糖尿病 (T1D) 问题的表现.
- 评估LLM对儿科T1D信息检索的有效性.
- 确定哪些LLM为T1D提供了最准确和最为患者友好的反应.
主要方法:
- 采用了一个横截面的,比较的研究设计.
- 从儿科内分泌学社交媒体调查中选择了20个常见的T1D问题.
- 五个LLM (ChatGPT-3.5,ChatGPT-4,ChatGPT-4o,双子座,双子座高级) 被五位儿科内分泌学家使用一般质量表 (GQS) 评估.
主要成果:
- 所有评估的LLM都回答了20个T1D问题.
- 聊天GPT-4o获得了最高的平均GQS得分 (3.78 ± 1.09),而双子座的得分最低 (3.40 ± 1.24).
- 与ChatGPT-3.5和Gemini相比,ChatGPT-4o,ChatGPT-4和Gemini Advanced提供了更高质量的答案,尽管整体差异在统计学上并不显著 (p=0.103).
结论:
- 所有LLM在回答儿科1型糖尿病 (T1D) 问题时表现相似.
- 像ChatGPT-4o和Gemini Advanced这样的高级LLM提供了高于平均水平,准确和患者友好的反应.
- 在儿童糖尿病护理中,LLM对医疗保健整合具有前景,等待持续评估和专门模型的开发.
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