Computer Science > Machine Learning
arXiv:2406.06007 (cs)
[Submitted on 10 Jun 2024 (v1), last revised 3 Nov 2024 (this version, v3)]
Title:CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models
Authors: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
View a PDF of the paper titled CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models, by Peng Xia and 23 other authors
View PDFHTML (experimental)Abstract:Artificial intelligence has significantly impacted medical applications, particularly with the advent of Medical Large Vision Language Models (Med-LVLMs), sparking optimism for the future of automated and personalized healthcare. However, the trustworthiness of Med-LVLMs remains unverified, posing significant risks for future model deployment. In this paper, we introduce CARES and aim to comprehensively evaluate the Trustworthiness of Med-LVLMs across the medical domain. We assess the trustworthiness of Med-LVLMs across five dimensions, including trustfulness, fairness, safety, privacy, and robustness. CARES comprises about 41K question-answer pairs in both closed and open-ended formats, covering 16 medical image modalities and 27 anatomical regions. Our analysis reveals that the models consistently exhibit concerns regarding trustworthiness, often displaying factual inaccuracies and failing to maintain fairness across different demographic groups. Furthermore, they are vulnerable to attacks and demonstrate a lack of privacy awareness. We publicly release our benchmark and code inthis https URL.
| Comments: | NeurIPS 2024 Datasets and Benchmarks Track |
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY) |
| Cite as: | arXiv:2406.06007 [cs.LG] |
| (orarXiv:2406.06007v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2406.06007 arXiv-issued DOI via DataCite |
Submission history
From: Huaxiu Yao [view email][v1] Mon, 10 Jun 2024 04:07:09 UTC (1,908 KB)
[v2] Wed, 30 Oct 2024 17:08:16 UTC (3,405 KB)
[v3] Sun, 3 Nov 2024 16:54:14 UTC (3,405 KB)
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View a PDF of the paper titled CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models, by Peng Xia and 23 other authors
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