Beyond the Hype: A dispassionate look at vision-language models in medical scenario
<|reference_start|>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 over-concentrate in evaluating VLMs based on simple Visual Question Answering (VQA) on multi-modality data, while ignoring the in-depth characteristic 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 unharmonised 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 and dataset will be available after the acceptance of this paper.<|reference_end|>
@article{nan2024beyond,
    title={Beyond the Hype: A dispassionate look at vision-language models in
  medical scenario},
    author={Yang Nan, Huichi Zhou, Xiaodan Xing, Guang Yang},
    journal={arXiv preprint arXiv:2408.08704},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08704},
    primaryClass={cs.CV cs.AI}
}