[2510.03160] SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus

Computer Science > Computer Vision and Pattern Recognition arXiv:2510.03160 (cs) [Submitted on 3 Oct 2025 (v1), last revised 5 Mar 2026 (this version, v3)] Title:SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus Authors:Ming Zhao, Wenhui Dong, Yang Zhang, Xiang Zheng, Zhonghao Zhang, Zian Zhou, Yunzhi Guan, Liukun Xu, Wei Peng, Zhaoyang Gong, Zhicheng Zhang, Dachuan Li, Xiaosheng Ma, Yuli Ma, Jianing Ni, Changjiang Jiang, Lixia Tian, Qixin Chen, Kaishun Xia, Pingping Liu, Tongshun Zhang, Zhiqiang Liu, Zhongyan Bi, Chenyang Si, Tiansheng Sun, Caifeng Shan View a PDF of the paper titled SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus, by Ming Zhao and 25 other authors View PDF HTML (experimental) Abstract:Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets. Clinical decision-making for spine disorders requires sophisticated reasoning across X-ray, CT, and MRI at specific vertebral levels. However, progress has been constrained by the absence of traceable, clinically-grounded instruction data and standardized, spine-specific benchmarks. To address this, we introduce SpineMed, an ecosystem co-designed with practicing spine surgeons. It features SpineMed-450k, the first large-scale dataset explicitly designed for vertebral-level reasoning across imaging modalities wit...