DERM-3R: A Resource-Efficient Multimodal Agents Framework for Dermatologic Diagnosis and Treatment in Real-World Clinical Settings

Abstract: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.

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2604.09596 [cs.AI]

(or arXiv:2604.09596v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.09596 Focus to learn more arXiv-issued DOI via DataCite

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