Cultivating Multimodal Intelligence: Interpretive Reasoning and Agentic RAG Approaches to Dermatological Diagnosis

• URL: http://arxiv.org/abs/2507.05520v1
• Date: Mon, 07 Jul 2025 22:31:56 GMT
• Title: Cultivating Multimodal Intelligence: Interpretive Reasoning and Agentic RAG Approaches to Dermatological Diagnosis
• Authors: Karishma Thakrar, Shreyas Basavatia, Akshay Daftardar,
• Abstract summary: The second edition of the 2025 ImageCLEF MEDIQA-MAGIC challenge focuses on multimodal dermatology question answering and segmentation.<n>This work addresses the Closed Visual Question Answering (CVQA) task, where the goal is to select the correct answer to multiple-choice clinical questions.<n>The team achieved second place with a submission that scored sixth, demonstrating competitive performance and high accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The second edition of the 2025 ImageCLEF MEDIQA-MAGIC challenge, co-organized by researchers from Microsoft, Stanford University, and the Hospital Clinic of Barcelona, focuses on multimodal dermatology question answering and segmentation, using real-world patient queries and images. This work addresses the Closed Visual Question Answering (CVQA) task, where the goal is to select the correct answer to multiple-choice clinical questions based on both user-submitted images and accompanying symptom descriptions. The proposed approach combines three core components: (1) fine-tuning open-source multimodal models from the Qwen, Gemma, and LLaMA families on the competition dataset, (2) introducing a structured reasoning layer that reconciles and adjudicates between candidate model outputs, and (3) incorporating agentic retrieval-augmented generation (agentic RAG), which adds relevant information from the American Academy of Dermatology's symptom and condition database to fill in gaps in patient context. The team achieved second place with a submission that scored sixth, demonstrating competitive performance and high accuracy. Beyond competitive benchmarks, this research addresses a practical challenge in telemedicine: diagnostic decisions must often be made asynchronously, with limited input and with high accuracy and interpretability. By emulating the systematic reasoning patterns employed by dermatologists when evaluating skin conditions, this architecture provided a pathway toward more reliable automated diagnostic support systems.

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