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arXiv · 2504.05575

A Lightweight Large Vision-language Model for Multimodal Medical Images

Belal Alsinglawi·Chris McCarthy·Sara Webb·Christopher Fluke·Navid Toosy Saidy
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Abstract

Medical Visual Question Answering (VQA) enhances clinical decision-making by enabling systems to interpret medical images and answer clinical queries. However, developing efficient, high-performance VQA models is challenging due to the complexity of medical imagery and diverse modalities. In this paper, we introduce a lightweight, multimodal VQA model integrating BiomedCLIP for image feature extraction and LLaMA-3 for text processing. Designed for medical VQA tasks, our model achieves state-of-the-art performance on the OmniMedVQA dataset. With approximately 8 billion parameters, it requires only two NVIDIA 40 GB A100 GPUs, demonstrating superior efficiency over larger models. Our results show 73.4% accuracy for open-end questions, surpassing existing models and validating its potential for real-world medical applications. Key contributions include a specialized multimodal VQA model, a resource-efficient architecture, and strong performance in answering open-ended clinical questions.

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Belal Alsinglawi, Chris McCarthy, Sara Webb, Christopher Fluke, Navid Toosy Saidy. 2025-04-08. A Lightweight Large Vision-language Model for Multimodal Medical Images. https://arxiv.org/abs/2504.05575

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