SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the
  SpineMed-450k Corpus
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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Ming Zhao
68e3295d73e20ab577841fdf
Wenhui Dong
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Yang Zhang
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Xiang Zheng
68e3295d73e20ab577841fe2
Zhonghao Zhang
68e3295d73e20ab577841fe3
Zian Zhou
68e3295d73e20ab577841fe4
Yunzhi Guan
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Liukun Xu
68e3295d73e20ab577841fe6
Wei Peng
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Zhaoyang Gong
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Zhicheng Zhang
68e3295d73e20ab577841fe9
Dachuan Li
68e3295d73e20ab577841fea
Xiaosheng Ma
68e3295d73e20ab577841feb
Yuli Ma
68e3295d73e20ab577841fec
Jianing Ni
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Changjiang Jiang
68e3295d73e20ab577841fee
Lixia Tian
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Qixin Chen
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Kaishun Xia
68e3295d73e20ab577841ff1
Pingping Liu
68e3295d73e20ab577841ff2
Tongshun Zhang
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Zhiqiang Liu
68e3295d73e20ab577841ff4
Zhongan Bi
68e3295d73e20ab577841ff5
Chenyang Si
68e3295d73e20ab577841ff6
Tiansheng Sun
68e3295d73e20ab577841ff7
Caifeng Shan
SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the
  SpineMed-450k Corpus
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.
SpineMed, an ecosystem with SpineMed-450k and SpineBench, addresses the lack of level-aware, multimodal datasets and benchmarks for AI-assisted diagnosis of spine disorders, improving model performance through fine-grained, level-specific reasoning.
vertebral-level reasoning X-ray CT MRI instruction data spine-specific benchmarks SpineMed SpineMed-450k SpineBench clinician-in-the-loop pipeline LLM generation method large vision-language models (LVLMs) fine-grained level-specific reasoning