DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making

2026-05-15 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Medical Devices & Health Technology · Depth: Expert, extended

Summary

DermAgent is a novel multi-tool agent designed for dermatological image analysis, addressing limitations of existing Multimodal Large Language Models (MLLMs) like insufficient domain grounding and hallucinations. It integrates seven specialized vision and language modules within a Plan–Execute–Reflect framework, providing stepwise, traceable diagnostic reasoning. Key components include complementary visual perception tools for morphological description, dermoscopic concept annotation, and disease diagnosis. To prevent hallucinations, DermAgent uses a dual-modality retrieval module that cross-references 413,210 diagnosed image cases and 3,199 clinical guideline chunks. A deterministic critic module further audits predictions via confidence, coverage, and conflict gates, triggering self-correction for inter-source disagreements. Experiments on five dermatology benchmarks show DermAgent outperforms state-of-the-art MLLMs and medical agent baselines, exceeding GPT-4o by 17.6% in skin disease diagnostic accuracy and 3.15% in captioning ROUGE-L.

Key takeaway

For Computer Vision Engineers developing medical diagnostic AI, DermAgent demonstrates a robust architecture to overcome MLLM limitations. You should consider implementing a multi-tool agentic system with external knowledge retrieval and a deterministic critic for self-correction to improve diagnostic accuracy, reduce hallucinations, and provide traceable reasoning in your applications.

Key insights

DermAgent uses a multi-tool, self-reflective agentic system with external knowledge retrieval to enhance dermatological diagnosis and mitigate MLLM hallucinations.

Principles

• Orchestrate specialized tools for complex tasks.
• Ground predictions in external, verifiable evidence.
• Implement deterministic self-correction for consistency.

Method

DermAgent operates via a Plan–Execute–Reflect loop, orchestrating seven specialist tools. A Chatbot plans tool calls, which are executed to update an evidence chain. A Critic module then audits this chain for confidence, coverage, and conflicts, triggering replanning if issues are found.

In practice

• Integrate Case RAG for image-based evidence.
• Utilize Guideline RAG for text-based clinical context.
• Employ a Critic module for post-hoc auditing.

Topics

• DermAgent
• Agentic Systems
• Dermatological Image Analysis
• Multi-Tool Reasoning
• Retrieval-Augmented Generation

Code references

• YizeezLiu/DermAgent

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.