Summary of Beyond the Hype: a Dispassionate Look at Vision-language Models in Medical Scenario, by Yang Nan et al.
Beyond the Hype: A dispassionate look at vision-language models in medical scenario
by Yang Nan, Huichi Zhou, Xiaodan Xing, Guang Yang
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed RadVUQA benchmark evaluates Large Vision-Language Models (LVLMs) across five dimensions: anatomical understanding, multimodal comprehension, quantitative and spatial reasoning, physiological knowledge, and robustness. The benchmark assesses LVLMs’ capabilities in radiological visual understanding and question answering, highlighting their critical deficiencies in multimodal comprehension and quantitative reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models (LVLMs) are powerful tools that can help doctors diagnose diseases more accurately. Right now, these models aren’t good enough at understanding medical images and text together. To fix this problem, researchers created a new test to see how well LVLMs do in understanding medical images and answering questions about them. They tested several models and found out they’re not very good at combining visual and linguistic information. This is important because doctors need these kinds of skills to diagnose diseases correctly. |
Keywords
» Artificial intelligence » Question answering
