皮肤病学实践中的ChatGPT和DeepSeek:人工智能模型是否遵守牛皮治疗指南?
Journal of drugs in dermatology : JDD
|February 5, 2026
概括
两个大型语言模型 (LLM),ChatGPT和DeepSeek,显示与牛皮治疗指南的总体一致性很高. 然而,人工智能驱动的建议需要临床监督,因为存在不一致性,特别是有限的证据.
科学领域:
- 医学中的人工智能.
- 皮肤病学和机器学习
- 坚持临床指南的遵守.
背景情况:
- 人工智能,特别是LLM,在医疗保健中越来越多地用于诊断和治疗规划.
- 在慢性皮肤病如牛皮症的AI建议的可靠性是不确定的.
- 评估AI与临床指导方针的结合对于安全的整合至关重要.
研究的目的:
- 评估ChatGPT和DeepSeek与美国皮肤病学会 (AAD) 牛皮治疗指南的一致性.
- 为了比较两个著名的LLM在坚持基于证据的皮肤病护理方面的表现.
- 确定LLMs在提供牛皮管理建议方面的潜在局限性.
主要方法:
- 根据2021年AAD-NPF牛皮指南,向ChatGPT-4和DeepSeek.com提出了31个问题.
- 答案被盲目的,经过董事会认证的皮肤科医生评估,以符合指导方针.
- 根据推强度分析了一致率,并使用Cohen的kappa系数评估了模型间的一致性.
主要成果:
- 聊天GPT和DeepSeek都实现了AAD指南的总体一致率为87.1%.
- 聊天GPT与中度建议 (87.5%与81.3%) 的一致性更高,而DeepSeek在有限建议 (66.7%与33.3%) 中表现出色.
- 两家LLM之间的模式间协议是适度的 (kappa = 0.43).
结论:
- 聊天GPT和DeepSeek在与基于证据的牛皮护理保持一致方面显示出显著的希望.
- 不一致性,特别是有限的证据,需要仔细的临床验证和监督.
- 人工智能可以成为 psoriasis 管理中的一个有价值的辅助工具,但需要对上下文敏感的应用程序来实现安全的整合.
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