Summary of Cares: a Comprehensive Benchmark Of Trustworthiness in Medical Vision Language Models, by Peng Xia et al.
CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
by Peng Xia, Ze Chen, Juanxi Tian, Yangrui Gong, Ruibo Hou, Yue Xu, Zhenbang Wu, Zhiyuan Fan, Yiyang Zhou, Kangyu Zhu, Wenhao Zheng, Zhaoyang Wang, Xiao Wang, Xuchao Zhang, Chetan Bansal, Marc Niethammer, Junzhou Huang, Hongtu Zhu, Yun Li, Jimeng Sun, Zongyuan Ge, Gang Li, James Zou, Huaxiu Yao
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
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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 paper, CARES, aims to comprehensively evaluate the trustworthiness of Medical Large Vision Language Models (Med-LVLMs) across five dimensions: trustfulness, fairness, safety, privacy, and robustness. The authors introduce a new benchmark, comprising approximately 41K question-answer pairs in both closed and open-ended formats, covering 16 medical image modalities and 27 anatomical regions. The analysis reveals that Med-LVLMs consistently exhibit concerns regarding trustworthiness, often displaying factual inaccuracies and failing to maintain fairness across different demographic groups. Additionally, the models are vulnerable to attacks and demonstrate a lack of privacy awareness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Med-LVLMs have been used in medical applications with great promise, but their trustworthiness remains unverified. The CARES paper evaluates Med-LVLMs on five important dimensions: trustworthiness, fairness, safety, privacy, and robustness. They found that these models often get facts wrong and are unfair to different groups of people. Also, they can be tricked into doing the wrong thing and don’t respect people’s privacy. |
