OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM
This addresses the problem of evaluating LVLMs in medical applications for researchers and practitioners, but it is incremental as it primarily provides a new benchmark rather than a novel method.
The authors tackled the lack of diverse medical image benchmarks for evaluating Large Vision-Language Models (LVLMs) by introducing OmniMedVQA, a comprehensive medical Visual Question Answering dataset sourced from 73 datasets, 12 modalities, and over 20 anatomical regions, and found that existing LVLMs, including medical-specialized ones, struggle with these tasks, often performing worse than general-domain models.
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in various multimodal tasks. However, their potential in the medical domain remains largely unexplored. A significant challenge arises from the scarcity of diverse medical images spanning various modalities and anatomical regions, which is essential in real-world medical applications. To solve this problem, in this paper, we introduce OmniMedVQA, a novel comprehensive medical Visual Question Answering (VQA) benchmark. This benchmark is collected from 73 different medical datasets, including 12 different modalities and covering more than 20 distinct anatomical regions. Importantly, all images in this benchmark are sourced from authentic medical scenarios, ensuring alignment with the requirements of the medical field and suitability for evaluating LVLMs. Through our extensive experiments, we have found that existing LVLMs struggle to address these medical VQA problems effectively. Moreover, what surprises us is that medical-specialized LVLMs even exhibit inferior performance to those general-domain models, calling for a more versatile and robust LVLM in the biomedical field. The evaluation results not only reveal the current limitations of LVLM in understanding real medical images but also highlight our dataset's significance. Our code with dataset are available at https://github.com/OpenGVLab/Multi-Modality-Arena.