Abstract
Our work introduces the DermETAS-SNA LLM Assistant that integrates Dermatology-focused Evolutionary Transformer Architecture Search with StackNet Augmented LLM. The assistant dynamically learns skin-disease classifiers and provides medically informed descriptions to facilitate clinician-patient interpretation. Contributions include: (1) Developed an ETAS framework on the SKINCON dataset to optimize a Vision Transformer (ViT) tailored for dermatological feature representation and then fine-tuned binary classifiers for each of the 23 skin disease categories in the DermNet dataset to enhance classification performance; (2) Designed a StackNet architecture that integrates multiple fine-tuned binary ViT classifiers to enhance predictive robustness and mitigate class imbalance issues; (3) Implemented a RAG pipeline, termed Diagnostic Explanation and Retrieval Model for Dermatology, which harnesses the capabilities of the Google Gemini 2.5 Pro LLM architecture to generate personalized, contextually informed diagnostic descriptions and explanations for patients, leveraging a repository of verified dermatological materials; (4) Performed extensive experimental evaluations on 23 skin disease categories to demonstrate performance increase, achieving an overall F1-score of 56.30% that surpasses SkinGPT-4 (48.51%) by a considerable margin, representing a performance increase of 16.06%; (5) Conducted a domain-expert evaluation, with eight licensed medical doctors, of the clinical responses generated by our AI assistant for seven dermatological conditions. Our results show a 92% agreement rate with the assessments provided by our AI assistant (6) Created a proof-of-concept prototype that fully integrates our DermETAS-SNA LLM into our AI assistant to demonstrate its practical feasibility for real-world clinical and educational applications.
Abstract (translated)
我们的工作介绍了一种名为DermETAS-SNA的LLM助手，它结合了以皮肤科为重点的进化Transformer架构搜索与StackNet增强的大规模语言模型。该助手能够动态学习皮肤疾病分类器，并提供医学信息描述，以便于医生和患者之间的沟通理解。 贡献包括： 1. 在SKINCON数据集上开发了一种ETAS框架，优化了一个针对皮肤病特征表示量身定制的Vision Transformer (ViT)，并为DermNet数据集中23个皮肤疾病类别中的每一个进行了二元分类器的微调，以增强分类性能。 2. 设计了一种StackNet架构，该架构整合了多个经过微调的二元ViT分类器，以此来增强预测稳健性，并减轻类不平衡问题。 3. 实施了一个RAG管道，名为皮肤病诊断解释和检索模型（DiExRet），利用Google Gemini 2.5 Pro LLM架构生成个性化的、基于上下文的信息的诊断描述和解释给患者，利用一个经过验证的皮肤科材料库。 4. 在23个皮肤疾病类别上进行了广泛的实验评估，展示了性能提升，实现了整体F1分数为56.30%，比SkinGPT-4 (48.51%) 提高了相当大的幅度（提升了16.06%）。 5. 进行了一项领域专家评估，八位持证医生对我们的AI助手生成的七种皮肤病情况下的临床响应进行了评价。结果显示，与我们AI助手提供的评估有92%的一致性。 6. 创建了一个概念验证原型，将我们的DermETAS-SNA LLM完全整合到我们的AI助手中，以展示其在现实世界临床和教育应用中的实际可行性。 这些成果展示了我们在皮肤科诊断辅助工具方面的创新工作及其对提高医学实践效率的潜在贡献。
URL
https://arxiv.org/abs/2512.08998
