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

• URL: http://arxiv.org/abs/2510.03160v2
• Date: Sat, 25 Oct 2025 03:34:09 GMT
• 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,
• Abstract summary: Spine disorders affect 619 million people globally and are a leading cause of disability.<n>We introduce SpineMed, an ecosystem co-designed with practicing spine surgeons.<n>It features SpineMed-450k, the first large-scale dataset explicitly designed for vertebral-level reasoning.
• Score: 39.664918145306366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 with over 450,000 instruction instances, and SpineBench, a clinically-grounded evaluation framework. SpineMed-450k is curated from diverse sources, including textbooks, guidelines, open datasets, and ~1,000 de-identified hospital cases, using a clinician-in-the-loop pipeline with a two-stage LLM generation method (draft and revision) to ensure high-quality, traceable data for question-answering, multi-turn consultations, and report generation. SpineBench evaluates models on clinically salient axes, including level identification, pathology assessment, and surgical planning. Our comprehensive evaluation of several recently advanced large vision-language models (LVLMs) on SpineBench reveals systematic weaknesses in fine-grained, level-specific reasoning. In contrast, our model fine-tuned on SpineMed-450k demonstrates consistent and significant improvements across all tasks. Clinician assessments confirm the diagnostic clarity and practical utility of our model's outputs.

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