发表机构
Centro de Informática, Universidade Federal de Pernambuco (UFPE); Hospital das Clinicas, Ebserh, UFPE（佩南布科联邦大学信息中心; 佩南布科联邦大学临床医院（Ebserh））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究系统评估通用与皮肤科专用模型在临床皮肤病变分类中的性能，量化分布偏移等挑战下的差距，旨在推动可靠且公平的AI系统临床应用。
AI 中文摘要
近年来，机器学习在皮肤病学中的应用大幅增长，已从概念验证研究迈向潜在的实际应用。然而，临床皮肤病学仍然是一个具有挑战性且尚未解决的问题。诊断评估往往具有模糊性，皮肤病变表现出高度变异性，加之采集方式、设备质量和患者人口统计学特征的差异，这些因素阻碍了开发适用于安全且公平的临床应用的稳健模型。为支持向实践的转化，系统评估当代模型在不同数据源间的泛化能力至关重要。在本工作中，我们在最新的皮肤病学数据集上对多种架构进行基准测试，涵盖皮肤镜图像和基于智能手机的临床照片。我们评估了近期通用型和医学视觉语言模型以及基础模型的稳健性，并将其与任务特定的皮肤病学分类器（包括基于嵌入的方法和卷积神经网络）进行比较。我们的研究提供了在分布偏移、模态变化和人口统计学变异性下模型性能的评估。通过量化当前最先进模型与临床部署要求之间的差距，我们旨在为开发可靠、可获取且临床适用的皮肤病学AI系统做出贡献。
英文摘要
The application of machine learning to dermatology has grown substantially in recent years, moving beyond proof-of-concept studies toward potential applications. However, clinical dermatology remains a challenging and still open problem. Diagnostic assessment is often ambiguous, and skin lesions exhibit high variability, compounded by differences in acquisition modality, device quality, and patient demographics. These factors hinder the development of robust models suitable for safe and equitable clinical use. To support translation into practice, it is essential to systematically evaluate how contemporary models generalize across heterogeneous data sources. In this work, we benchmark a diverse set of architectures on recent dermatology datasets, spanning dermoscopic images and smartphone-based clinical photographs. We assess the robustness of recent general-purpose and medical vision-language models, as well as foundation models, and compare them against task-specific dermatology classifiers, including embedding-based approaches and convolutional neural networks. Our study provides an evaluation of model performance under distribution shifts, modality changes, and demographic variability. By quantifying the gap between current state-of-the-art models and the requirements of clinical deployment, we aim to contribute to the development of reliable, accessible, and clinically applicable AI systems for dermatology.
Comments10 pages, 1 figure, 3 tables, approved at MICCAI 2026
