Search papers, labs, and topics across Lattice.
The authors introduce SpineMed, an ecosystem for AI-assisted diagnosis of spine disorders, comprising SpineMed-450k, a large-scale, vertebral-level instruction dataset spanning multiple imaging modalities, and SpineBench, a clinically-grounded evaluation framework. They curated SpineMed-450k from diverse sources using a clinician-in-the-loop pipeline with a two-stage LLM generation method to create high-quality, traceable data. Evaluation of several advanced LVLMs on SpineBench revealed weaknesses in fine-grained, level-specific reasoning, while a model fine-tuned on SpineMed-450k showed significant improvements, validated by clinician assessments.
Current vision-language models stumble on subtle spine diagnoses, but a new dataset and benchmark expose these weaknesses and pave the way for clinically useful AI.
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.
