SpineBench：一种临床显著、层级感知的基准，由SpineMed-450k语料库驱动
SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus
AI总结：
SpineBench通过SpineMed-450k语料库驱动，针对脊柱疾病提供临床显著的层级感知基准，评估模型在多模态数据下的细粒度推理能力。
摘要：
脊柱疾病影响全球6.19亿人，是导致残疾的主要原因之一，然而AI辅助诊断仍受限于缺乏层级感知、多模态数据集。脊柱疾病的临床决策需要在特定椎体层级上对X光、CT和MRI进行复杂推理。然而，进展受限于缺乏可追溯、基于临床指导的指令数据和标准化的、针对脊柱的基准。为此，我们引入SpineMed，由临床脊柱外科医生共同设计的生态系统。它包含SpineMed-450k，这是首个大规模数据集，专门用于跨成像模态的椎体层级推理，包含超过450,000个指令实例，以及SpineBench，一个基于临床的评估框架。SpineMed-450k从多样化的来源中收集，包括教科书、指南、开放数据集和约1000个去标识化的医院病例，使用带有两个阶段LLM生成方法（草稿和修订）的临床人员在循环中的管道进行整理，以确保高质量、可追溯的数据用于问答、多轮咨询和报告生成。SpineBench在临床显著的轴上评估模型，包括层级识别、病理评估和手术计划。我们对几种最近先进的大型视觉-语言模型（LVLMs）在SpineBench上的全面评估揭示了在细粒度、层级特定推理中的系统性弱点。相比之下，我们在SpineMed-450k上微调的模型在所有任务上都表现出一致且显著的改进。临床评估确认了我们模型输出的诊断清晰度和实际实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
