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在皮肤诊断中使用人工智能彻底改变皮肤病理学:范围审查

Rawan Rammal¹, Ahmad Mohy U Din¹, Tanvir Alam¹

¹College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Frontiers in medicine

|March 16, 2026

概括

人工智能 (AI) 模型在诊断皮肤疾病方面表现有前途,但目前的模型在罕见疾病方面扎. 加强临床验证和多样化的数据集对于在医疗保健中广泛采用至关重要.

科学领域:

皮肤病学和人工智能研究
医学图像分析医学图像分析
计算病理学计算病理学

背景情况:

人工智能 (AI) 越来越多地被纳入皮肤疾病的诊断和治疗.
现有的AI模型通常依赖于卷积神经网络 (CNN) 和视觉转换器 (ViT).
大型语言模型 (LLM) 正在成为交互性皮肤学分析的新架构.

研究的目的:

在皮肤疾病诊断和治疗中对AI模型进行范围审查.
分析AI架构的演变,包括基于CNN,ViT和LLM的模型.
评估AI模型在各种皮肤病,包括罕见疾病的诊断效果.

主要方法:

遵守PRISMA-ScR (系统性审查的首选报告项目和范围审查的元分析扩展) 准则.
2017年至2024年间发表的12篇文章的系统搜索和选择.
分析AI模型类型,数据源 (私人临床数据,公共数据集如ISIC,MoleMap) 和疾病覆盖范围.

主要成果:

CNN和ViT模型是普遍存在的,最近基于LLM的模型增加了,如SkinGPT和Gemini.
人工智能模型在常见的皮肤疾病中表现出高效,但在罕见或代表性不足的疾病中表现下降.
研究利用了各种数据,但大多数人工智能模型需要进一步的临床验证和监管监督.

结论:

人工智能模型为改善皮肤病理学,增强病变分类和早期检测提供了巨大的潜力.
当前的人工智能模型往往过于通用,并且缺乏适用于各种皮肤病应用的稳定性.
未来的发展需要更强大的,经过临床验证和道德监管的人工智能工具来整合医疗保健.

关键词:

在这里,我们可以看到AIAIAI.在美国,CNN是CNN.在法学士 (LLM) 课程中.这里是ViT ViT ViT 皮肤学皮肤学

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