Med-GRIM: Enhanced Zero-Shot Medical VQA using prompt-embedded Multimodal Graph RAG

• URL: http://arxiv.org/abs/2508.06496v1
• Date: Sun, 20 Jul 2025 06:44:12 GMT
• Title: Med-GRIM: Enhanced Zero-Shot Medical VQA using prompt-embedded Multimodal Graph RAG
• Authors: Rakesh Raj Madavan, Akshat Kaimal, Hashim Faisal, Chandrakala S,
• Abstract summary: A representation model, BIND: BLIVA Integrated with Dense.<n>Dense, extends prior multimodal work by refining the joint embedding space through dense, query-token-based encodings.<n>This refined encoder powers Med-GRIM, a model designed for medical VQA tasks.<n>By assigning distinct roles to each agent within the VQA system, Med-GRIM achieves large language model performance at a fraction of the computational cost.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An ensemble of trained multimodal encoders and vision-language models (VLMs) has become a standard approach for visual question answering (VQA) tasks. However, such models often fail to produce responses with the detailed precision necessary for complex, domain-specific applications such as medical VQA. Our representation model, BIND: BLIVA Integrated with Dense Encoding, extends prior multimodal work by refining the joint embedding space through dense, query-token-based encodings inspired by contrastive pretraining techniques. This refined encoder powers Med-GRIM, a model designed for medical VQA tasks that leverages graph-based retrieval and prompt engineering to integrate domain-specific knowledge. Rather than relying on compute-heavy fine-tuning of vision and language models on specific datasets, Med-GRIM applies a low-compute, modular workflow with small language models (SLMs) for efficiency. Med-GRIM employs prompt-based retrieval to dynamically inject relevant knowledge, ensuring both accuracy and robustness in its responses. By assigning distinct roles to each agent within the VQA system, Med-GRIM achieves large language model performance at a fraction of the computational cost. Additionally, to support scalable research in zero-shot multimodal medical applications, we introduce DermaGraph, a novel Graph-RAG dataset comprising diverse dermatological conditions. This dataset facilitates both multimodal and unimodal querying. The code and dataset are available at: https://github.com/Rakesh-123-cryp/Med-GRIM.git

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