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.

通过扩展视觉语言模型技术，本文提出了一种名为SkinGEN的诊断生成框架，通过使用稳定扩散 (SD) 方法从视觉语言模型的诊断结果生成参考演示，从而增强用户的视觉可解释性。实验证明，SkinGEN显著改善了用户对视觉语言模型预测的理解，增加了对诊断过程的信任，为更加透明和用户中心的视觉语言模型应用在皮肤科学及其他领域铺平了道路。

SkinGEN：一个可解释的皮肤科诊断生成框架，配有交互式视觉语言模型