Beyond the Hype: A Dispassionate Look at Vision-Language Models in Medical Scenario

Recent advancements in large vision-language models (LVLMs) have demonstrated remarkable capabilities across diverse tasks, garnering significant attention in AI communities. However, their performance and reliability in specialized domains such as medicine remain insufficiently assessed. In particular, most assessments overconcentrate on evaluating VLMs based on simple visual question answering (VQA) on multimodality data while ignoring the in-depth characteristics of LVLMs. In this study, we introduce RadVUQA, a novel radiological visual understanding and question answering benchmark, to comprehensively evaluate existing LVLMs. RadVUQA mainly validates LVLMs across five dimensions: 1) anatomical understanding, assessing the models' ability to visually identify biological structures; 2) multimodal comprehension, which involves the capability of interpreting linguistic and visual instructions to produce desired outcomes; 3) quantitative and spatial reasoning, evaluating the models' spatial awareness and proficiency in combining quantitative analysis with visual and linguistic information; 4) physiological knowledge, measuring the models' capability to comprehend functions and mechanisms of organs and systems; and 5) robustness, which assesses the models' capabilities against unharmonized and synthetic data. The results indicate that both generalized LVLMs and medical-specific LVLMs have critical deficiencies with weak multimodal comprehension and quantitative reasoning capabilities. Our findings reveal the large gap between existing LVLMs and clinicians, highlighting the urgent need for more robust and intelligent LVLMs. The code is available at https://github.com/Nandayang/RadVUQA.