Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA

• URL: http://arxiv.org/abs/2510.13856v1
• Date: Sun, 12 Oct 2025 07:03:58 GMT
• Title: Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA
• Authors: A H M Rezaul Karim, Ozlem Uzuner,
• Abstract summary: Medical Visual Question Answering (MedVQA) enables natural language queries over medical images to support clinical decision-making and patient care.<n>We present the MasonNLP system, which employs a general-domain, instruction-tuned large language model with a retrieval-augmented generation (RAG) framework.<n>Best-performing system ranked 3rd among 19 teams and 51 submissions with an average score of 41.37%.
• Score: 0.6015898117103068
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical Visual Question Answering (MedVQA) enables natural language queries over medical images to support clinical decision-making and patient care. The MEDIQA-WV 2025 shared task addressed wound-care VQA, requiring systems to generate free-text responses and structured wound attributes from images and patient queries. We present the MasonNLP system, which employs a general-domain, instruction-tuned large language model with a retrieval-augmented generation (RAG) framework that incorporates textual and visual examples from in-domain data. This approach grounds outputs in clinically relevant exemplars, improving reasoning, schema adherence, and response quality across dBLEU, ROUGE, BERTScore, and LLM-based metrics. Our best-performing system ranked 3rd among 19 teams and 51 submissions with an average score of 41.37%, demonstrating that lightweight RAG with general-purpose LLMs -- a minimal inference-time layer that adds a few relevant exemplars via simple indexing and fusion, with no extra training or complex re-ranking -- provides a simple and effective baseline for multimodal clinical NLP tasks.

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