利用大型语言模型为儿童慢性疾病护理提供信息:一个横截面研究
Syed Furrukh Jamil1,2,3, Nada N Alshathri4,2, Seham S Alsalamah4,2
1College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia furrukh.jamil@doctors.org.uk.
BMJ paediatrics open
|August 14, 2025
概括
像ChatGPT和谷歌双子座这样的大型语言模型提供了准确的患者教育关于腹腔疾病和1型糖尿病. 然而,为了更好的可访问性,它们的可读性需要改进.
科学领域:
- 人工智能在医学中的应用
- 儿科健康教育教育 儿科健康教育
背景情况:
- 慢性儿科疾病,如心脏病和1型糖尿病等,需要让患者获得可访问的患者教育.
- 大型语言模型 (LLM) 为可扩展的健康信息传播提供了潜力.
研究的目的:
- 评估ChatGPT 3.5,ChatGPT 4.0和谷歌双子在生成关于心脏病和1型糖尿病的教育内容方面的表现.
- 评估对常见问题LLM产生的答案的准确性,全面性,可读性和一致性.
主要方法:
- 分析了76个与腹腔疾病和1型糖尿病相关的常见问题 (FAQ).
- 基于预定义的标准对ChatGPT 3.5,ChatGPT 4.0和Google Gemini响应进行比较评估.
主要成果:
- 所有评估的LLM在他们的答案中都表现出高度的准确性和全面性.
- 在这些模型中,ChatGPT 4.0提供了最易读的内容.
- 所有模型在测试的常见问题中都表现一致,但整体可读性需要改进.
结论:
- 临床医学仪表显示出作为慢性儿科疾病患者教育的补充工具的巨大潜力.
- 需要进一步开发,以提高LLM产生的健康信息对患者的可读性和可访问性.
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