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对白病患者教育的准确性和完整性评估大型语言模型:比较分析

Jieyan Su¹, Xi Yang¹, Xiangying Li¹

¹Department of Dermatology, Central Hospital Affiliated to Shandong First Medical University, Jinan, People's Republic of China.

Clinical, cosmetic and investigational dermatology

|October 29, 2025

概括

大型语言模型 (LLM) 可以准确地回答基本的白风问题,但难以提供复杂的治疗建议. 人类监督对于这些AI工具的医疗决策至关重要.

科学领域:

皮肤病学和人工智能研究
医疗教育技术技术医学教育
患者信息系统患者信息系统

背景情况:

Vitiligo 在心理上对患者产生重大影响,创造了对可访问的教育资源的需求.
目前针对白风患者的教育资源不足以满足需求.
大型语言模型 (LLM) 显示了增强白风患者教育的潜力.

研究的目的:

评估OpenAI o1,DeepSeek-R1和Grok 3的适用性,准确性和局限性,用于白风患者教育.
为了比较不同LLM在处理患者关于白风的询问方面的表现.
评估LLMs是否适合提供与白风相关的健康信息.

主要方法:

皮肤病专家制定了16个白风问题 (描述性/推性,基本/高级).
三个LLM (OpenAI o1,DeepSeek-R1,Grok 3) 回答了这些问题.
皮肤科医生和患者评估了LLM响应的准确性,可理解性和相关性;还进行了可读性分析.

主要成果:

所有的LLM都对基本描述性问题表现出令人满意的准确性和可理解性.
士学位的表现各不相同,复杂的推查询,特别是治疗建议的不一致性.
在准确度方面,DeepSeek-R1超过了OpenAI o1 (p=0.042);所有模型的阅读难度都提高了,DeepSeek-R1的阅读难度最低.

结论:

LLM准确地回答简单的白风问题,但难以处理复杂的治疗建议.
目前的LLM可能会提供错误的白风治疗建议,需要改进过和人类监督.
强制性人类监督对于涉及LLM产生的建议的医疗决策至关重要.

关键词:

聊天GPT 聊天在GPT 聊天在DeepSeek搜索中深入搜索.格洛克·格洛克 (Grok Grok) 是一个古老的语言.大型语言模型.患者教育患者教育在白风,白风.

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