With the widespread application of artificial intelligence (AI), particularly deep learning (DL) and vision-based large language models (VLLMs), in skin disease diagnosis, the need for interpretability becomes crucial. However, existing dermatology datasets are limited in their inclusion of concept-level meta-labels, and none offer rich medical descriptions in natural language. This deficiency impedes the advancement of LLM-based methods in dermatological diagnosis. To address this gap and provide a meticulously annotated dermatology dataset with comprehensive natural language descriptions, we introduce SkinCAP: a multi-modal dermatology dataset annotated with rich medical captions. SkinCAP comprises 4,000 images sourced from the Fitzpatrick 17k skin disease dataset and the Diverse Dermatology Images dataset, annotated by board-certified dermatologists to provide extensive medical descriptions and captions. Notably, SkinCAP represents the world's first such dataset and is publicly available at https://huggingface.co/datasets/joshuachou/SkinCAP.

翻译：随着人工智能（AI），特别是深度学习（DL）和基于视觉的大语言模型（VLLMs）在皮肤病诊断中的广泛应用，对可解释性的需求变得至关重要。然而，现有的皮肤病学数据集在包含概念级元标签方面存在局限，并且没有一个能提供丰富的自然语言医学描述。这一缺陷阻碍了基于LLM的方法在皮肤病学诊断中的进展。为了填补这一空白，并提供一个带有全面自然语言描述的、经过精细标注的皮肤病学数据集，我们引入了SkinCAP：一个带有丰富医学描述的多模态皮肤病学数据集。SkinCAP包含从Fitzpatrick 17k皮肤病数据集和Diverse Dermatology Images数据集中获取的4,000张图像，由经过委员会认证的皮肤科医生进行标注，以提供详尽的医学描述和说明。值得注意的是，SkinCAP是世界上首个此类数据集，并已在https://huggingface.co/datasets/joshuachou/SkinCAP公开提供。