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

TL;DR

Med-GRIM introduces a modular, knowledge-injected approach for medical visual question answering that enhances accuracy and efficiency without extensive fine-tuning, supported by a new dermatology dataset.

Contribution

It presents Med-GRIM, a novel zero-shot medical VQA model using graph-based retrieval and prompt engineering, and introduces DermaGraph, a dermatology dataset for multimodal research.

Findings

Med-GRIM achieves high accuracy with low computational cost.

The model effectively integrates domain knowledge via prompt-based retrieval.

DermaGraph enables scalable research in dermatological multimodal applications.

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