Poster Fri, Apr 24, 2026 • 11:15 AM – 1:45 PM PDT Pavilion 4 P4-#5304

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

Ming Zhao ⋅ Wenhui Dong ⋅ Yang Zhang ⋅ wangyou ⋅ 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 ⋅ Chen Qixin ⋅ Xia Kaishun ⋅ Pingping Liu ⋅ Tongshun Zhang ⋅ ZhiqiangLiu ⋅ Zhongan Bi ⋅ Chenyang Si ⋅ Tiansheng Sun ⋅ Caifeng Shan

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 $\sim$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.