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大型语言模型的系统性审查及其在皮肤病学中的影响

Miłosz Lewandowski¹, Julia Kropidłowska^1,2, Alexandra Kvinen^1,2

¹Department of Dermatology, Venereology and Allergology, Faculty of Medicine, Medical University of Gdansk, Gdansk, Poland.

The Australasian journal of dermatology

|April 6, 2025

概括

大型语言模型 (LLM) 正在通过分析各种患者数据来改进诊断和治疗来改变皮肤病学. 这篇评论探讨了当前的LLM应用,挑战和该领域的未来潜力.

科学领域:

计算语言学计算语言学
医疗信息学医疗信息学
皮肤病学皮肤病学

背景情况:

大型语言模型 (LLM) 代表了计算语言学的重大进步.
这些模型越来越多地集成到各种部门,包括医疗保健,特别是皮肤病学.
LLM提供了从复杂的患者数据中获得更深入的见解的潜力.

研究的目的:

系统地评估皮肤病学中公开可访问的大型语言模型应用程序.
确定当前的研究,关键挑战,以及皮肤病学LLM的未来方向.
弥合了解LLMs在皮肤病学实践中的影响和潜力的差距.

主要方法:

对皮肤病学中大型语言模型应用发表的研究进行系统审查.
分析LLM如何整合不同类型的数据,如人口统计,临床图像和遗传信息.
评估LLM在诊断,治疗建议和患者教育方面的能力.

主要成果:

在皮肤病学中,LLM可以提高诊断准确度和治疗规划.
应用包括分析临床照片,患者访谈和人口统计数据.
在患者教育和改善医生与患者之间的互动方面,LLM显示出潜力.

结论:

大型语言模型提供了通过数据分析和决策支持来推进皮肤病护理的巨大潜力.
解决目前的障碍对于实现皮肤病学LLM的全部好处至关重要.
未来的研究应该专注于进一步整合和验证临床环境中的LLM应用.

关键词:

聊天GPT 聊天在GPT 聊天人工智能的人工智能是人工智能.一般皮肤病学皮肤病学大型语言模型.医学皮肤病医学皮肤病学审查审查审查审查审查审查系统性审查系统性审查

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