DERM-3R：一种资源高效的多模态代理框架，用于皮肤病诊断和治疗的现实世界临床应用
DERM-3R: A Resource-Efficient Multimodal Agents Framework for Dermatologic Diagnosis and Treatment in Real-World Clinical Settings
AI总结：
本文提出DERM-3R框架，通过三个协作代理解决皮肤病诊断中的细粒度病变识别、多视角病变表示和综合推理问题，展示了其在资源受限下的高效性能。
摘要：
皮肤病对全球人口造成重大负担，影响数十亿人并显著降低生活质量。尽管现代疗法能快速控制急性症状，但长期疗效常受限于单一目标模式、复发性和对系统性共病的忽视。传统中医（TCM）通过辨证论治提供互补的全面方法，但实践受非标准化知识、不完整的多模态记录和专家推理的可扩展性限制。本文提出DERM-3R，一种资源高效的多模态代理框架，用于在数据和计算受限的情况下建模TCM皮肤病的诊断和治疗。基于现实世界的工作流程，将决策制定重新表述为三个核心问题：细粒度病变识别、多视角病变表示与专家级病理学建模、以及综合推理用于辨证论治和治疗规划。DERM-3R由三个协作代理组成：DERM-Rec、DERM-Rep和DERM-Reason，分别针对该流程的每个组件。基于轻量级多模态大语言模型，并在103个现实世界TCM银屑病病例上部分微调，DERM-3R在皮肤病推理任务中表现优异。使用自动指标、LLM-as-a-judge和医生评估显示，尽管数据和参数更新有限，DERM-3R在性能上匹配或超越了大型通用多模态模型。这些结果表明，结构化、领域感知的多代理建模可以成为复杂临床任务在皮肤科和整合医学中的实用替代方案，而非粗暴扩展。
英文摘要：
Skin diseases impose a substantial and growing global health burden. Modern dermatologic therapies control acute manifestations rapidly, but single-target treatment paradigms, recurrent disease courses, and neglected systemic comorbidities limit their long-term effectiveness. Traditional Chinese medicine (TCM) offers a complementary, holistic approach through syndrome differentiation and individualized treatment, yet its clinical practice is hindered by non-standardized knowledge, incomplete multimodal records, and the difficulty of scaling expert-driven reasoning. We propose DERM-3R, a resource-efficient multimodal agents framework that models TCM dermatologic diagnosis and treatment under limited data and computational resources. We decompose and redefine real-world dermatologic decision-making into three essential issues: fine-grained lesion recognition, multi-view lesion representation with specialist-level pathogenesis modeling, and holistic clinical reasoning for syndrome differentiation and treatment planning. DERM-3R comprises three collaborative agents, DERM-Rec, DERM-Rep, and DERM-Reason, each addressing one of these issues. Built on a lightweight multimodal large language model and fine-tuned by partial-parameter finetuning on 103 real-world TCM psoriasis cases, DERM-3R performs strongly across multiple dermatologic reasoning tasks. Automatic metrics, LLM-as-a-Judge assessments, and human doctors show that, despite extremely limited training data and parameter updates, DERM-3R matches or even surpasses hundred-billion-parameter general-purpose multimodal models such as GPT-5.1 and Gemini-3-Flash. Our results indicate that structured, domain-aware multi-agent modeling is an effective alternative to brute-force scaling for complex clinical tasks, offering a practical and scalable paradigm for multimodal AI in dermatology and integrative medicine.
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