  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.
