  With the continuous advancement of vision language models (VLMs) technology,
remarkable research achievements have emerged in the dermatology field, the
fourth most prevalent human disease category. However, despite these
advancements, VLM still faces "hallucination" in dermatological diagnosis, and
due to the inherent complexity of dermatological conditions, existing tools
offer relatively limited support for user comprehension. We propose SkinGEN, a
diagnosis-to-generation framework that leverages the stable diffusion (SD)
method to generate reference demonstrations from diagnosis results provided by
VLM, thereby enhancing the visual explainability for users. Through extensive
experiments with Low-Rank Adaptation (LoRA), we identify optimal strategies for
skin condition image generation. We conduct a user study with 32 participants
evaluating both the system performance and explainability. Results demonstrate
that SkinGEN significantly improves users' comprehension of VLM predictions and
fosters increased trust in the diagnostic process. This work paves the way for
more transparent and user-centric VLM applications in dermatology and beyond.
