大型语言模型可以回答手掌透症患者的健康教育问题吗? 一项对ChatGPT和DeepSeek进行比较的研究
Shajing Fan1, Xiaoqing Liu2, Min Tang1
1Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
Digital health
|November 17, 2025
概括
这项研究比较了ChatGPT和DeepSeek,以回答手掌过的健康问题. 两种模型都显示出适应性,ChatGPT在英语和DeepSeek在中文中表现出色,尽管专家审查至关重要.
科学领域:
- 医疗保健中的人工智能
- 医疗教育 技术 技术 医学教育
背景情况:
- 手掌透症是一种导致手部过度出汗的疾病.
- 有效的患者教育对于管理手掌透症至关重要.
- 大型语言模型 (LLM) 显示了健康信息传播的潜力.
研究的目的:
- 评估ChatGPT和DeepSeek在响应有关手掌水症的健康教育查询方面的适应性.
- 为了比较这些LLM在英语和中文语言背景中的表现.
主要方法:
- 根据临床指导方针制定了17个关于手掌过的健康教育问题.
- 聊天GPT和DeepSeek被查询了这些问题.
- 十二位胸部外科专家使用五分利克特尺度对LLM的反应进行了评分.
主要成果:
- 聊天GPT和DeepSeek都表现出良好的适应能力,用于掌膜水症健康教育.
- 在英语中,ChatGPT的表现优于DeepSeek,获得了10个回答的专家评分.
- 在中文中,DeepSeek表现出了卓越的表现,两种模式在10个响应中都获得了满分.
结论:
- 聊天GPT和DeepSeek是解决手掌多汗症患者教育问题的有效工具.
- 模型性能因语言而异:英语的ChatGPT,中文的DeepSeek.
- 人类专家审查对于确保临床实践的准确性和可靠性至关重要.
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