SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus
Authors: Ming Zhao
Wenhui Dong
Yang Zhang
XiangZheng
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
Venue: ICLR 2026 Poster (2026)
Keywords: VLM;LLM;Medical
Primary area: datasets and benchmarks

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.

Published as a conference paper at ICLR 2026
SPINEBENCH: A CLINICALLY SALIENT, LEVEL-AWARD BENCHMARK POWERED BY THE SPINEMED-450K COR- PUS
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 and Caifeng Shan
ABSTRACT
Spine disorders affect 619 million people globally and are leading cause of disabil- ity, yet Al-assisted diagnosis remains limited by the lack of leve-awaar,, multimodal datasets. Clinical decision-makin soning across X-ray, CT, and MRI at specific vertebral levels. However, progress has been constrained by the absence of traceable, clinically- data and standardized, spine-specific benchmarks. To address this, we introduce SpineMed, 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-grounde
for spine disorders requires sophisticated rea-
instruction
evaluation framework. SpineMed-450k is cu- rated from diverse sources, including textbooks, guidelines, open datasets, and ~ ~1,000 de-identified hospital cases, using clinicianiin-th-llowe in-the-loop pipeline with a two-stage LLM generation method (draft and revision) to ensure high-quality, traceable data for guestion-answering. multi-turn consultations, and report gen- eration. SpineBench evaluates models on clinically salient axes, including level identification, pathology assessment, and surgical planning. Our comprchensive evaluation of several recently advanced large rision-language models (LVLMs) on SpineBench reveals systematic weaknesses in fine-grained, level-specific reason- ing. 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.
INTRODUCTION
Qwen2.5-VLLT
(AZIB)
4:96
4.45
03
4.55
Z
Treatment Recommendations
Reasuning and
Image
Imaging Report
Dingnosis
Prognosis
QA
Medical Report Generation
Figure 1: Benchmark performance of SpineGPT
Equal Contributions. CCCorespponding author: suntiansheng-Ge @ 1enieeennn@116....0 163.com; cfshan @nw..edu.nnnnnn... The complete author details are provided Appendix

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Medical Corsuitation
SpineMed-4455 450K
Chrical Guddeies
Pobli dotests
Expert Gerveree
Clac rapert
Clocial hacords
Multi diverhhy
Arzotstion
453K
Synthaass
Mediool
35K
pracesss
Auti-Tuck
Medicll Image Andyots
Diogeatiic Assictrant
Bosk Aspesiment
operstive Movegemont
Scurce
Figure 2: Overview of SpineMed-450k 450k. Training data was curated from textbooks, public datasets, clinical records, medical guidelines, and hospitals. The process involved data preprocessing, anno- tation generation, and a final clinician review. Our dataset comprises four types: multi-choice QA, open-ended QA, multi-round dialogues, and reports.
Spinal disorders (Ferreira et al. 2023). including degenerative diseases (like dise herniation) (Dydyk et al. 2017), deformities (like scoliosis) (Negrini et al. 2018), trauma (fractures) (Vaccaro et al. 2013), J and inflammatory conditions (Taurog et al. 2016), are a major driver of pain, disability, and surgical care worldwide. A key challenge in their management is diagnostic complexity. Unlike many other disorders, spinal conditions typically cannot be precisely diagnosed using a single imaging modality. It often requires clinicians to perform level-aware, multimodal reasoning: integrating findings from X-ray, CT, and MRI to pinpoint pathology at specific vertebral levels, grade severity, and plan interventions (Teichner et al.) (2025). The precision of this interpretation directly impacts patient outcomes and neurological safety. Although advanced AI holds great promise for augmenting this demanding workflow (lbrahim et al., (0255), its potential has been hindered. Fortunately, such clinical tasks can significantly benefit from advanced Al capabilities (Lee et al.] (022bb. Yet progress is constrained not by model capacity. but by the absence of traceable instruction data and standardized, clinically validated benchmarks tailored to spine workflows (Lee et al.) 2024b)) Equally important, prior efforts rarely embed clinicians throughout the pipeline, limiting practical utility. We present SpineMed: a comprehensive effort consisting of SpineMMed-450k, provenance-rich instruction corpus for spine diagnosis and planning, and SpineBench, targeted evaluation suite that help to evaluate the effectiveness of different Al-based spine diagnosis. To our best knowledge, this is current largest-scale Spinal diagnosis and treatment dataset. Both were co-designed with spine clinicians (radiologists and surgeons) to reflect real decision points. SpineMed 440k aggregates materials from textbooks, surgical guidelines, expert consensuses, question banks, open spine datasets (e.g., Spark, VerSe) (Alibaba Cloud Tianchi 2020 [Sekuhoyina et al. [022]) open-access case reports (Europe PMC) (Consortium) 2015), and ~ 1,000 de-identified hospital cases. Throughout curation, clinicians (i) defined inclusion criteria and task taxonomies; (ii) vetted imaging selections from hospital cases to prioritize views most informative for diagnosis and surgical planning; and (iii) specified failure modes that instruction data must surface. To minimize hallucinations and preserve traceability, our pipeline (a) extracts figures and text with P Paddles eOCR (Du et al., 2020); (b) binds images to their local textual context via caption- pattoon--atemnntto regex matching that anchors each figure to its surrounding paragraph; and (c) distills high-quality multi-turn consultations, and report inneerationnhhruuu a two-stage LLM process (draft - revision with explicit prompts and logs). Clinicians review and refine prompt policies and revision criteria to align with reporting standards.
open-ended QA,
BeineBench operationalizes evaluation across clinically relevant axes axes-inaging report, diagnosis, patient guidance, evidence-bane -ased treatment, technical feasibility, risk prognosis, coverage, relevance, granularity, and interpretabillty. Its item design, error taxonomy, and rubrics were developed with clinician input to emphasize fine-grained, anatomy-centric reasoning and the kinds of mistakes that matter in practice.
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To characterize the state of the field, we evaluate dozen of contemporary large vision language models (LVLMs) (OpenAl 2023a 6 Hurst et 2024 Google 2025a Sellergren et al., 2025a XAI 2025 Anthropic 2025 Bai et al. 2025; Hong et all][2225 Wang al. (025a), both general-purpor and medical. Our evaluation reveals significant weaknesses in fine fine-grained, level-specific diagnosis and open-ended clinical reasoning, particularly in the handling of complex multi-image tasks. Build- ing on these insights, we introduce a fine-tuned spine model SpineGPT trained on SpineMed-450k that delivers consistent improvements on SpineBench as shown in Figure | Clinicians assess ex- emplar outputs for decision relevance, underscoring the practical value of targeted, evidence- linked instruction data. Our contributions are as follows:
Clinician-in-the-loop dataset and benchmark. We release SpineMed-450k, more than 450,000 instruction instances spanning multiple-choice, open-ended QA, multi-ture turn consul- tations, and report generation- integration and two-stage report refinement, together with SpineBench, a level-aware benchmark co-designed with clinicians and enriched with ~~10000 real hospital cases.
via specialist-suupported pipeline with anatomical
Comprehensive evaluation. We benchmark dozens of open-source LVLMs across closed/open tasks using clinician-shaped taxonomies and rubrics, surfacing systematic failure modes in spine reasoning.
A practical baseline model. We propose fine-tuned spine LVLM trained on SpineMed- 450k that achieves consistent gains on SpineBench; exemplar outputs receive clinician feedback on diagnostic clarity and planning utility, establishing high-utility baseline for future research.
SPINEMED-450N DATASET
Overview. The SpineMed-450k dataset was constructed through a meticulous "clinician-in-the loop" pipeline designed to ensure clinical accuracy and relevance. This pipeline integrates four core stages: (1) Dataset collection, (2) Structured Information Extraction, (3) Data De-ieentification and Cleaning, and (4) Dataset Generation. (5) Annotation of the spinal diagnostic report.
2.1
DATA COLLECTION
To build complete and comprehensive dataset for spinal diagnosis and treatment, we collected data
from a variety of sources (Chen et [224aa] Wei & Hwei, 2024, Wu al. 2025 Chen et al. (024b)
Existing general- -eneral-purpose large vision- language models (Hurst et al. [0224] Google 2025a b) Deng
et al. 2023 Ullah et al. 2024 AlSaad et al. (024) and even medical large language models (Li et al.
2023 Wang et al. 2025b Wu et al. 2024 Lin et al. 2025 Lu et al. 2024 Niu et al. 2025 Nath et al.
2025a Seyfioglu et al. 2024] Lai et al. 2025 Xu et al. 2025) are trained on generic medical data
(Chen et al. 2024a b. Xie al. 2024a), which often lacks the high-quality, specialized data needed for orthopedics (Dng al. 20233 Ullah et al. 2024). To train an effective large model for spinal care, we first compiled high-quality, general orthopedic dataset covering multiple domains, including Spine Surgery, Foot and Ankle Surgery, Orthopedic Trauma, and Hand and Upper Extremity Surgery.
integrated materials from variety of sources, including textbooks, surgical guidelines, expert consensuses, question banks, open-access case reports from Europe PMC (Consortium 2015), open single-modality spine datasets (Alibaba Cloud Tianchi 2020 (Sekuboyina ett al. 2021) (e.g., Spark, VerSe), and approximately 1 ,,000 de-identified multimodal hospital cases collected from various hospitals. This data covers a wide range of modalities. including text, CT, MRI, X-ray, and tables. We track the provenance (dataset IDs/DOIs, case identifiers) for every derived item. Where possible, we adopt upstream datasets with permissive licenses and clear terms of reuse. Clinicians defined the inclusion criteria and, for hospital cases, selected the most decision iifooinatioomoivieee images (e.g., MRI target sequences, key CT levels) to serve as the foundation for downstream tasks.
As shown in Figure
2.2 DATASET CURATION
Structured Information Extraction To accurately extract comprehensive information from aca- demic sources, we employed PaddleOCR (Du et al. 2020) to parse PDF documents and images from textbooks and literature. The output, containing both recognized text and layout analysis, was
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Task diagnosis
Context Pairing
SpineMed-4500
15k inage context pairs
Spins
report
Foot Anile Sung
-56k
treatment
De De-identification
Trouna
-53k
Hond & Uppar Sung.
+00k
Muscolstllleo
-26
Type
Decuplicaton
kaggle
Degenerutive Spine
Expert LLM Medel
Aouit Joint Rec.
-17k
Deforeshiee
Textual
Visual QA
OCR Recogrition
Open- anded 162k Multi-chaioe: 2284
Opes ende--ddddd-- - 25k Nulti-choios: 20k
P
Modality
Metadata
Medical Consultation
Medical Report
X-ray
Total dillggus +1.1k
Total hospitril coses: 834
CT
PAIRED
197k 12k
Average images per dislogue 16
Average images per report:
MRI
Dato registrction
Image Selection
Total images: -18k
Total imeges: -12k
Figure Generation pipeline of SpineMed-450k. The pipeline involves data preprocessing (including de-identification. deduplication, and OCR) followed by expert LLM-driven curation. This process generates 450k items for tasks like QA, medical reports, and consultations across various orthopedic subspecialties.
exported into Markdown format. This approach effectively preserved the structural integrity of the documents, including tables, figure placements, and overall layout. Furthermore, to ensure precise mapping between figures, their captions, and corresponding contextual descriptions in the text, we developed a novel algorithm termed Picture Context Matching. Subsequently, we employed GPT-SSmini(OpenA. 2025a) to conduct semantic consistency check, rigorously filtering out in- stances where the visual content did not align with the retrieved context. The technical details of this algorithm elaborated in the Appendix B
Data De-identification and Cleaning This stage focused on processing data sourced from collection of clinical records in hospitals. We first performed rigorous de-identification process, removing all sensitive and personally identifiable information (PII), such as patient IDs and physical examination details under HIPAA. We also filtered out irrelevant images, such as post-operative photos and non- non-diagnostic tables. Subsequently, GP'--5-mini GPPTS--min(OpenAA 2025a) was utilized conduct a fine-grained classification of the data, ensuring the dataset's purity by excluding non-orthopedic cases. As shown in Figure 2] the orthopedic domain categorized into 7 classes, with the spine sub-domain further divided into 14 distinct classes A detailed statistical overview of the dataset distribution across these categories is presented in Figure
Dataset Generation In close collaboration with medical experts, designed comprehensive annotation schema to generate high-quality, multi-task training data. The annotation process was tailored the data source: (1) From External Knowledge Sources (e.g., Textbooks): We generated bilingual (Chinese and English) and multimodal (text and image-based) questions in both multiple- choice and open-ended formats using
2025a) with carefully designed prompts. (2) From Opened-sping Datasets: We processed two open-source spinal datasets, Spark and Verse, to generate multi-ture question- soestiio--nddammee and-answer dialogues that simulate pettor-patient interactions. These datasets consist mainly of unimodal 3D) image slices (CT and MRI). To ensure consistency, we standardized the inputs by adaptively sampling 25 slices per case under clinical expert supervision. From this, we created over 300 simulated consultations train models in their conversational abilities within spinal scenarios. (3) From Real Clinical Records: We created multiple-choice questions, multi-turn conversational datasets for patient interviews, and comprehensive spinal diagnostic reports via locally deployed GLMM-45V et al. 2025) to ensure data security. For prompt design, please refer to the Appendix H
Annotation of the spinal diagnostic report A comeestone of our dataset is the generation of detailed spinal diagmostic reports. In this process, we utilized real clinical reports from hospitals, incorporating physician recommendations, aimed at simulating a complete clinical workflow: (1) Structured Imaging Findings: Analyze the provided medical images and distill key radiological evidence that supports the final diagnosis. (2)
design reports that encompass six dimensions, all
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Al-Assisted Diagnosis:
Formulate diagnostic conclusion and articulate the reasoning process based on the synthesis of clinical data and imaging analysis. (3) Treatment Recommendations: This section is bifurcated to address different audiences. Patient-Centric Advice: Explain the rationale for the recommended surgical procedure in clear, non-technical language. Physician-Centrie Rationale: Provide robust, guideline-based decision tree justify the surgical selection from clinical perspective. (4) Risk and Prognosis Assessment: Conduct an objective evaluation of the potential risks and expected outcomes associated with the proposed surgical plan. (5) Postoperative Issue Management: Predict potential post-surgical complications for specific procedures and develop corresponding management strategies. (6) Diagnostic Rationale and Disclaimer: Provide complete diagnostic and surgical pacision-makingg chain and disclaimer statement. Report examples are provided in the Appendix G
2.3 COMPARISON WITH EXISTING BENCHMARKS
Table 1:
Comparison of SpineMed-450k with existing spine imaging datasets. While prior datasets focus specific perception tasks, work introduces the first multimodal instruction-tuni tunnnn corpus designed for full-spectrun clinical reasoning.
Modality
Scale
Task
Workflow Coverage
Output
LumforDSCC (Richanls 1025
MRI
2k Patients
Claviffiation
Specific (Stenosis Grading)
Class Tabels
BUU LSPINE
Ray
3.6k Patients
Detection
Specitic (Spondylolitthnn
Cooddnstes/Labels
Verie 2020 [Li30 M02
CT
300 Patients
Segneesttion
Specitii (Anutoury)
Masks
Lumbur Sprik
SE caaa et 2024
MR1
218 Patients
Segmensation
Specific (Stracture)
Vinel Marks
Spari (luazhha) Tilien 2020
MR.MRI
150 Panents
Classitiation
Specilie (Disease ID)
Class Labels
SpineMidd 45% (Ours)
Multimedal
450k
Clinical Reasoning
Spell-peettrm
Instruction Pairs
(XR. CT. MRI Text)
Instructions
(Dig + Trist - Progrosis)
(Irage, Text
As illustrated in Table existing datasets such as VerSe(Liebl et al. 2021 and RSNA(Richards et al. 2025) are predominantly unimodal and designed for low-level perception tasks like segmentation, detection, or simple classification. While these datasets serve effective tools for specific anatomical localization or binary disease identification, they fundamentally fail to capture the holistic context required for complex clinical Decision-making limiting their utility in training models for high- level diagnostic synthesis. contrast, SpineMed-4500 represents a significant paradigm shift from "Tool Al" "Collaborator Al". Our dataset distinguishes itself through three key dimensions: 1. Multimodal Synthesis, requiring the integration of X-ray, CT, and MRI to mirror real-world cross- modal validation; 2. Cognitive Depth, supporting level-aware reasoning rather than simple label outputs; 3. Workflow Completeness, covering the full patient journey with grounded instructions for Structured Imaging Findings, AI Diagnosis, Treatment Recommendations, and Risk Assessment. This effectively fills the cognitive gap left by perception- focussed datasets.
3 DATA STATISTICS
SpineMed-450K a large-scale multimodal training dataset for orthopedic spine knowledge in large language models, characterized by strong traceability, comprehensive coverage, diverse question types, and rich modalities.
3.1 DISEASE DIVERSITY COVERAGE
As shown in Figure b). SpineMed-450K encompasses seven common orthopedic subspecialties, including Spine Surgery, Foot and Ankle Surgery, and Orthopedic Trauma, with spinal diagnostic data accounting for 47% of the orthopedic data. Furthermore, the spinal diagnostic data includes 14 spine subconditions such as cervical degenerative spine disease and idiopathic scoliosis. We performed sampling on each spinal diagnostic dataset to ensure uniform distribution across all disease categories.
3.2 PATIENT SOURCE DIVERSITY
As illustrated in Figure 4 a), our data originates from 1,000 real clinical cases collected from 11 leading expert hospitals. These data span the recent three years and encompass patients of different genders, various age groups, and diverse physical conditions. To protect privacy, personal information has been de-identified. personal information. Given the varying surgical volumes across different
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CHTTROORAAC SERPPEES
SPINE SURGERY
(x) Hogital Distrbution
(b) Distriburion of Disease
Multimodal
CH ENG
Number ltems
91.8%
8,2%
39.7%,
60.35
Open-ended 82.4%s
17.0%
57.3%
62.7%
Consultation
report
Total
87.31%
38.5%
Number of Takkens
12003
15000
Modalites
Disiibution
Figure 4: Statistics of SpineMed 450k..445kkko-5 (a) Distribution of medical records across various hospitals. (b) The prevalence of various orthopedic and spinal diseases. (c) Distribution of different modals and languages. (d) Benchmark token length distribution: blue (non-report tokens), pink (report tokens).
hospitals, the largest hospital contributes 33% of the data while the smallest contributes 1%. These valuable real patient data provide crucial evidence for accurately representing the authentic conditions of spine patients.
3.3
DATA SOURCE AND QUESTION TYPE DIVERSITY
Table 2: Dataset statistics categorized by data source and split.
Split
Literature
Textbook
Case Report Question Bank
Open Source
Hospital
Total
Train
6,450
377,212
61,453
1,087
304
9,668
456,174
Test
203
101
250
Total
6.467
377,415
61,554
1,090
304
9,918
456,748
As shown in Table 2 our data derives from ma- jor sources: Literature, Text- books, Case Reports, hospi- tals, and others. Textbooks, being the primary knowledge source for physicians, con- stitute the largest proportion with 377k entries, while hospital data, though valuable, is limited in quantity, with 9,668 data points generated from nearly 1,000 real cases. As presented in Table and Figure (e.), question types categorized into pure text QA, multimodal QA, medical consultations, and clinical reports, with multiple-choice questions comprising the largest proportion. For evaluation convenience, our test set includes only multiple-choir and clinical report formats.
Table Dataset distribution across domains and task types.
Split
Multiple- choice
Open- ended
Consultation
Report
Train
248,789
197,413
1,138
734
Test
487
87
Total
249.276
197,413
1,138
821
3.4
DATA TYPE DIVERSITY
Our dataset incorporates multiple authentic data types including patient physical examination infor- mation, patient consultation records, X-rays, CT seans, and MRI images. Due to variations hospital facilities and patient conditions, the collected data differs for each case, which introduces modeling challenges but enables our trained models to more closely approximate real clinical scenarios faced by physicians.

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4 SPINEBENCH
4.1 BENCHMARK CONSTRUCTION
Data Sampling The SpineBench constructed by sampling from the SpineMed-450k dataset. Following the original distribution of SpineMed-450k, we sampled 500 multiple-choice questions and 100 medical reports. This subset incorporates 14 spinal sub-diseases and data from multiple sources (see Appendix F for details).
Data Validation To ensure the integrity of SpineBench, rigorous review process was implemented involving a team of 17 board-certified orthopedic surgeons. To mitigate bias and ensure objectivity, the surgeons were divided into three independent groups. Each group collaboratively validated the quality of the questions. Erroncous question-answer pairs were corrected, and questions deemed unsuitable for the evaluation set were removed. Ultimately. SpineBench comprises 487 high-quality multiple-chote -hhicce questions and 87 report generation prompts.
4.2 EVALUATION METRICS
Table 4: Evaluation criteria for Al-generated clinical reports across five key dimensions
Report Section
Evaluation Criterion
Key Assesament Focus
1. Structured Imaging Report (SIP)
Imaging Report (1-5 pts)
Accurccy of findings, clinical significance, quantitative descriptions
Al-Assisted Diaguosis (AAD)
Diagnosis pls)
Primary diagnosis correctness, differential diaguoses, clinical ressoning
Patient Guidance (1-5 pts)
Language clarity, empushy, putient reasourance
III. Treatment Recommendations (TR)
Hvidence- Based Plan (1-5 pis)
Rationale, individualization guideline consistency
Tochnical Feasibility (1-5 pts)
Surgcaal details, complication prevention, backup plans
IV. Risk & Prognosis Management (RPM)
Risk- Risk-Prognen Prognosis Mgrnt (1-5 pis)
Perioperative planning, follow-up schedule, safety protocols
Coverage (1-5 pis)
Compleeeaccs of evideace identification and explanation
Relevance (1-5 pas)
core diagnosis without irrelevant content
Reasoning & Disclaimer (RD)
Granolarity (1-5 pos)
Precision and quanitative detail sufficiency
Explanation (1-5 pis)
Logical coberence and reasoning chain clarity
Under the careful design and guidance of our medical team, We propose a comprehensive evalua- tion framework that integrates three complementary assessment dimensions to measure the overall performance of AI systems in spinal diagnostic tasks:
3 Σ
Score
wk Pk
(1)
where P1, P2. and P3 represent the performance scores for text-only multiple-choice questions, multimodal multiple-choice questions, and diagnostic report generation, respectively. The weights Wp are dynamically determined based on the sample sizes:
N
Wk
N
where N₂ denotes the number of samples in each evaluation category. This data-driven weighting scheme ensures statistical reliability while maintaining balanced representation across all assessment dimensions.
The diagnostic report score P3 is computed using our expert-calibrated framework:
5
P3 = 20 x
85j
i=1
where scores are normalized to a 0-100 scale for consistency across all metrics and s,y denotes the score for dimension in section i, n, represents the number of dimensions in section i. This unified scoring system enables direct comparison of model capabilities across diverse clinical tasks, from basic diagnostic reasoning complex report generation.
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5
SPINEGPT: A SPECIALIZED CLINICAL COLLABORATOR
In this section, we introduce SpineGPT to rigorously validate the efficacy of SpineMed-450k,
5.1 IMPLEMENTATION DETAILS
We employed the -iven2.5-VLL-777 Instruc et al. (2025) as our founda- tional architecture. All training phases were executed on a high-perfore -erfforrformncc computational node equipped with 8 NVIDIA A100 GPUs. leveraging the ms-swift framework for efficient fine- tuning. To balance training efficiency with memory constraints across differ- ent stages, we dynamically adjusted the DeepSpeed optimization strate-
Table 5: Training configurations across different stages.
Configuration
Stage-1
Stage-2
Stage-3
PubMedVision 150k
Spine-chat (+reasoning)
orthopedics-230k
Spine-open Spine-choice
Datasets
MedThoughts-SK
Spine-report (+reasoning)
Medical-BR I-Distilll
medical-ol -l--cssooinn
Learning Rate
lc-5
le-6
Max Length
16,384
16,384
49,152
DeepSpeed
7ero2
Zem2
Zero3
Epochs
1
gies. As detailed in Table S for the initial stages involving shorter sequences, we utilized DeepSpeed Zero2; for the final stage requiring extensive context modeling (up to 49k tokens), we transitioned to DeepSpeed Zero3 offloading. The global batch size was optimized per stage to maximize GPU utilization while maintaining training stability.
5.2 CURRICULUM LEARNING
This study employs a curriculum learning framework for subsequent training phases, aimed at enhancing the model's applicability and proficiency in the field of orthopedic spine care. The training process divided into three stages, each integrating distinct datasets and training strategies to progressively strengthen the model's performance in spinal health.
General and Orthopedic Foundational Learning In this initial stage, utilized several publicly available medical text datasets, including medical-ol -eaal-oo--rasonng-SSF- (Chen et al. (2024a). Medical- R1-Distill-Data (Chen al.) 20224),, and MedThoughts-8K (hw hweil, 2025). Additionally, incorporated a diverse set of 15,000 multimodal instruction fine-tuning samples uniformly sampled from PubMedVision (Chen al. 2024b). The primary objective during this phase to develop the model's s foundational capabilities in the medical field and to enhance its performance across various contexts. Subsequently, we trained on data from the SpineMed-450k dataset that pertained to non-spinal categories. Our findings indicate that this non-spinal data significantly improved the model's i performance on the SpineBench benchmark, highlighting the importance of broadening the knowledge base to enhance task-specific performance.
Specialized Learning in Spinal Health In this phase, we concentrated on all data pertinent to spinal health. Furthermore, we extracted selection of multiple-choiee and open-ended questions to construct long reasoning chains, with the objective of enhancing the model's proficiency in the domain of spinal surgery.
Enhancement of Report Generation and Conversational Abilities Finally, we conducted further training through multi-turn dialogues, report generation, and datasets comprising long-chain reasoning instructions. The goal of this stage to develop the model's advanced language comprehension and generation abilities, particularly in the contexts of dialogue interaction and report creation.
EXPERIMENTS
6.1
COMPARISON AND ANALYSIS ON SPINEBENCH
The evaluation results in Table 6 reveal severe limitations of current vision-language models (OpenAl 2025a Hurst et al.) 2024 Google 2025a) in medical domain applications. Large-scale source models perform particularly poorly: despite having 72B parameters, Qwen2.5-V1.-22B (Bai et al.)
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Table 6: Performance comparison of LVLMs on close-ended QA and medical report generation tasks.
Close-Ended QA
Medical Report Generation
Model
Size
Avg.
Text
Image
Avg.
SIP
AAD
TR
RPM
RD
Sum
Proprietary LVLMs
GPT5
87.41
79.97
84.46
4.54
4.51
4.53
4.69
4.64
91.60
85.54
03
86.73
82,38
85.01
4.39
4.25
4.34
4.43
4,42
87.32
85.36
Demini-2.5-PPrg
88.44
88.60
88.50
4.55
4.51
4.68
4.79
4.80
93.32
89.23
Claude4
79.59
79.79
79.67
3.96
4.08
4.04
4.44
4.41
83.72
80.28
GPT-4o
86.73
81.70
84.74
3.16
3.03
3.30
3.46
64.04
81.60
GPT5-mini
89.12
80.83
85.83
4.55
4,48
4.60
4.98
4.78
93.56
87.01
Gemini-24 2.5-Flash
83.67
80.83
82.55
4,43
4.29
4.57
4.88
4.75
91.68
83,93
Open-source LVLMs
GLM-4.5V
21B
85.71
81.35
83.98
3.85
3.78
3.83
4.05
4.30
79.24
83.26
Qwen2.5-VLL-72B
72B
84.69
79.79
82.75
3.14
3.03
3.12
3.23
3.42
63.80
79.88
Linshu-32B
32B
81.29
76.68
79.47
3.05
3.05
3.23
3.49
3.47
65.16
77.30
Medgemma-27B
27B
83.33
80.83
82.34
2.88
3,49
3.38
4.14
3.65
70.16
76.66
HuatuoGPT-7B
7B
75.85
80.83
77.82
2.42
3.37
2.87
54.0
74.21
Qwen2.5VL-7B
7B
75.51
74.09
74.95
2.27
2.39
2.80
3.26
2.92
54.52
64.74
Ours
7B
89.46
84.46
87.89
4.15
4.10
4.41
4.54
4.62
87.24
87.44
2025) achieves only 79.88% average performance and mere 63.80 cumulative score medical report generation, far below practical application requirements. Crucially, domain-speciff pre- training alone proves insufficient: the medical-specialle achieves an even lower average 76.66%, trailing our model by over 10 points despite being nearly 4 times larger. Even the best-performing open-source model GLM-4.5V (Hong et al. 2025 (83.26%) exhibits a nearly 6-point gap compared to the leading proprictary model Gemini 2.. Pro (89.23%) pronounced in medical report generation, where proprietary models exceed 85 points while open-source models struggle to reach 80. Additional medical report results are in the Appendix D
Beedgema-227B(Seleeee
et al. 2025b)
This gap is
Pervasive deficiency in cross-modas alignment. Nearly all models exhibit varying degrees of performance degradation on multimodal tasks. Among open-source models, GLM-4.5V shows 4.36-point gap between text (85.71 (1) and image (81 (81.35%) modalities; 4.90-point gap. Even proprietary models suffer from this issue, with GPT5 dropping from 87.41% on text to 79.97% on images, gap of 7. 7.44 percentage points. This cross-modal performance disparity reflects fundamental inadequacies medical image understanding and vision-language alignment in existing models, limiting their application in clinical scenarios requiring comprehensive analysis of medical images and textual information.
5-vn2.55 exhibits
Our method achieves breakthrough performance among open-source models. We achieve 87.44% average score, outperforming all open- open-source models by 4.18+ points and exceeding multiple proprietary models close-ended QA (87.89 Claude4's 79.67%, GPT-4o's 84.74%). Our text-only QA (89.46%) surpasses all models including GPT5 (87.41%)
Efficiency and Clinical Utility. While we acknowledge that SpineGPT (87.44%) slightly trails the absolute SOTA model, Gemini-2.5-PPr (89.23%) this comparison highlights remarkable efficiency: our 7B-parameter model uses less than 7% of the parameters compared Gemini-2.5-PH (which exceeds 100B parameters), yet achieves ~98% of its performance, Notably, SpineGPT also outperforms other top-tier models such as GPT-4o (81.6) 1.00)). This efficiency translates directly to clinical utility, as our lightweight 7B model is safe and efficient enough for local deployment within hospital firewalls, ensuring data privacy without reliance on external cloud APIs.
6.2 HUMAN-EXPERT AGREEMENT ANALYSIS
To validate LLM-based evaluation approach, we conducted human-expert validation study by sampling cases from our dataset for blind expert scoring. Figure BigureB]show shows the correlation analysis between L.LM and expert scores across ten evaluation dimensions. The results demonstrate strong

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Published as a conference paper at ICLR 2026
Figure 5: Consistency evaluation of large models and scores given by medical experts
alignment with Pearson correlation coefficients ranging from 0.382 to 0.949, with most dimensions showing correlations above 0.7. These findings validate that our automated LLM scoring serves as reliable proxy for expert judgment.
6.3 ABLATIONS OF SPINEGPT
Limitations General
Medical Data. As shown in Table 7 models trained exclusively large-scale general medical data (row 2) exhibit significant perfor- mance degradation (74.95 vs. 65.31) on SpineBench compared to the baseline model (row 1). This demon- strates that models trained on such data are insufficient
Table 7: Performance comparison of models on close-ended QA tasks.
Training Data
Close-Ended QA (%)
Model
General
No-Spine
Spine
Text
Image
Avg.
Qwen 2.5 VL-7B
75.51
74.09
74.95
SpineGPT
64.27
62.69
65.31
SpineGPT
82.99
80.83
82.14
SpineGPT
87.76
86.01
87.07
SpineGPT
83.67
77.20
81.11
SpineGPT
89.46
84.46
87.89
for specialized spine diagnostics. The incorporation of the non-spine orthopedic subset derived from our SpineMed-450k corpus (row 3) yields substantial performance improvements (82. 14 vs. 74.95), validating the importance of domain-aligned training data. Notably, training exclusively the spine subset (row 4) achieves an impressive average score of 87.07, reaching nearly 99% of the full model's performance. This confirms that our high-density spinal instruction data is the decisive factor. Furthermore, the full multi-stage curriculum (Row 6) incorporates this data to reach the peak performance of 87.89, substantial enhancement the configuration relying solely general medical and orthopedic priors (row 5, 81.11).
7 CONCLUSIONS, LIMITATIONS, AND FUTURE WORK
We introduced SpineMeed4400k, provenance-rich instruction corpus for level-aware spine diagnosis and planning, and SpineBench, level-aware benchmark co-designed with clinicians. Experiments on SpineBench reveal consistent weaknesses of contemporary open-source LVLMs. Our fine-tuned model achieves 87.44% performance, substantially outperforming open-source alternatives and demonstrating that specialized instruction data enables clinically relevant Al capabilities for complex anatomical reasoning tasks.
Limitations and Future Work. Future work will expand datasets, train larger models beyond 7B parameters, incorporate reinforcement learning techniques, and provide comprehensive direct com- parisons with leading proprictary models including GPT-4 and Gemini to establish clear performance benchmarks.
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LCKNOWLEDGEMENTS
This work was supported by the National Key Research and Development Program of China under Grant 2022YFC2407200, and in part by Sanyou Medical, and in part by cash and in-kind funding from Nanjing Kunpeng& Ascend Center of Cultivation and industry partner(s).
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APPENDIX
Contents
Checklist
17
A.1 Ethics Statement
17
A.2 Reproducibility Statement
17
A.3 LLM Usage
18
B Contributions and Acknowledgments
18
C Related Work
18
Performance Comparison
20
Picture Context Matching Algorithm
20
Detailed Distribution Analysis of SpineBench
21
Quantitative Comparison of SpineGPT with GPT-40
22
H Prompts
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A CHECKLIST
A.1 ETHICS STATEMENT
This work strictly adheres to the ICLR Code of Ethics. To address potential concerns regarding data provenance, patient privacy, and the redistribution public or copyrighted materials, we explicitly outline our comprehensive compliance protocols below.
Our data collection strictly adheres institutional, legal, and copyright frameworks. Patient privacy is guaranteed through rigorous de-identification procedures. Furthermore, to prevent the unauthorized redistribution of copyrighted texts or publicly available benchmark datasets, we employ a "metadata pointer" mechanism and strictly adhere to the principles of transformative use. Specifically, we do not redistribute any raw public medical images, copyrighted paragraphs, original figures, or sensitive clinical records. A detailed breakdown of our data sources, their respective scales, and the specific compliance mechanisms employed is summarized in Table 8.
Table 8: Overview of Data Sources, Scale, and Compliance Mechanisms
Category
Scale
Source
Compliance Redistribution Strategy
Clinical
9,918
1l authorized tertiary or- thopedics centers (retro- spective)
Zero re-identification risk via 2-stage de- identification (admin info erasure + secure OCR masking). No Raw Redistribution: Only a rep- resentative de-identified subset is released under Controlled Access requiring a Data Use Agree- ment (DUA).
Data
Open Datasets
304
Public spine imaging
No Image Redistribution: Striet adherence original terms. We release only instruction an- notations via a Metadata Pointer mechanism. requiring users to map our scripts to original images downloaded directly from official reposi- tories.
benchmarks (VerSe, Spark)
Literature & Reports
68,021
Europe PMC Open Ac- cess subset
API Access: Gathered via official REST(ul API. Strict filtering applied for Open Access licenses that explicitly permit academic text and data min- ing.
Textbooks & Guidelines
377,415
Renowned orthopedics textbooks & top Spine journals
Transformative Use:
Extracted solely for fac- tual medical concepts. diagnostic logic, and rea- soning chains to guide LLM synthesis. Abso- lutely copying or redistrihution of original copyrighted text or figures.
Question Banks
1,090
Publicly accessible med- ical exams
Transformative Use: Utilized purely as struc- tural templates for MCQ reasoning. The final output consists entirely of LLM-generate syn- thetic data, constituting academic fair use.
A.2 REPRODUCIBILITY STATEMENT
To maximize transparency and guarantee reproducibility while strictly adhering to institutional data governance and copyright laws, we adopt tiered open-source release strategy. We will fully open- source the SpineBench evaluation framework, all codebase implementations (training, inference, and evaluation scoring), and the complete SpineMed-450k instruction annotations (JSON files comprising QA pairs and reasoning chains). To prevent the unauthorized redistribution of copyrighted source images from public benchmarks (e.g., VerSe, Spark), we do not release the raw image files. Instead, mechanism, providing explicit data indices that allow researchers
we employ "Metadata-Pointe to map our open-source instructions to the original images acquired directly from their official repositories. Regarding the highly sensitive proprietary hospital data, full open-sourcing is legally prohibited. However, to ensure the verifiability of our claims, we will release a representative, fully de-identified clinical subset under Controlled Access model governed by Data Use Agreement
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(DUA). This released subset, in conjunction with the public data pointers, provides the exact necessary resources to fully reproduce all SpineBench evaluation metrics reported in this paper.
A.3 LLM USAGE
Large Language Models (LLMs) were used aid in the writing and polishing of the manuscript. Specifically, we used an LLM to assist in refining the language, improving readability, and ensuring clarity in various sections of the paper. The model helped with tasks such sentence rephrasing. grammar checking, and enbancing the overall flow of the text.
important to note that the LLM was not involved in the ideation, research methodology, or experimental design. All research concepts, ideas, and analyses were developed and conducted by the authors. The contributions of the LLM were solely focused on improving the linguistic quality of the paper, with no involvement in the scientific content or data analysis.
The authors take full responsibility for the content the manuscript, including any text generated or polished by the LLM. We have ensured that the I.I.--enerated text adheres to ethical guidelines and does not contribute to plagiarism or scientific misconduct.
B CONTRIBUTIONS AND ACKNOWLEDGMENTS
1. Ming Zhao, R Lab, Jilin University
2. Wenhui Dong, n Lab, Nanjing University
Yang Zhang, The Fourth Medical Center of People's Liberation Army General Hospital
4. Xiang Zheng, nO Lab, Institute of Automation, Chinese Academy of Sciences
Zhonghao Zhang, 3 Lab. Ningxia University
6. Zi An Zhou, T3 Lab, Zhejiang University
Yunzhi Guan, Huashan Hospital, Fudan University
8. Liukun Xu, The Fourth Medical Center of People's Liberation Army General Hospital
9. Wei Peng. x² Lab
10. Zhaoyang Gong, Huashan Hospital, Fudan University
11. Zhicheng Zhang, The Fourth Medical Center of People's Liberation Army General Hospital
12. Dachuan Li, Huashan Hospital, Fudan University
13. Xiaosheng Ma, Huashan Hospital, Fudan University
14. Yuli Ma. Sanyou Medical
15. Jianing Ni, 73 Lab
16. Changjiang Jiang, Wuhan University
17. Lixia Tian, Beijing Jiaotong University
18. Qixin Chen, The Second Affiliated Hospital, Zhejiang University
19. Kaishun Xia, The Second Affiliated Hospital, Zhejiang University
20. Pingping Liu, Jilin University
21. Tongshun Zhang, Jilin University
22. Zhiqiang Liu, π Lab, Huazhong University of Science and Technology
23. Zhongan Bi, 3 Lab, Zhejiang University
24. Chenyang Si, Nanjing University
25. Tiansheng Sun, The Fourth Medical Center of People's Liberation Army General Hospital
26. Caifeng Shan, Nanjing University
C RELATED WORK
The landscape of medical AI rapidly evolving, moving from broad, general-purpose models to highly specialized systems designed for clinical utility. Our work is situated within this trend, addressing a critical gap in the high-stakes field of spine surgery.
From Generalist Models to Domain Adaptation. Recent advances in Large Vision-Language Models (LVLMs), such as GPT-4V (OpenAI, 2023) and Gemini2.5 --prr (Google, 2025a), have demonstrated significant progress in multimodal tasks including image and video( Yang al., 2023; Team et al., 2023; Zheng et al., 2023; Jiang et al., 2025; Guo et al., 2025a: Zhu et al., 2024; Jiang al., 2026). However, when applied to the medical domain, their generalist nature becomes
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distinct limitation. Multiple evaluations consistently show that while promising, these models lack the nomain-specifee expertise required for complex diagnostic tasks, performing below the level of human specialists (AISaad et al., 2024). This inherent limitation of generalist models has fueled a clear and necessary trend toward specialization. In response, specialized medical LVLMs like LLaVA-Med (Li et al., 2023) and PMC-LLaMA (Wu al., 2024) have been developed, fine-tuned on large biomedical corpora. Nevertheless, this approach still has shortcomings. For instance, in spinal diagnosties, a critical task is the synthesis of data from multimodal imaging-such as X-ray, CT, and MRI-to formulate a single, "level-aware" diagnosis. This integrative reasoning process, which requires localizing findings to specific vertebral levels, is a clinical skill that cannot be acquired from static, descriptive datasets alone. This further underscores core principle: for high-stakes clinical applications, deep, narrow expertise is far more valuable than broad, superficial general knowledge. A powerful example validating this principle is OralGPT (Hao et al., 2025), model trained on a small, highly curated dataset of intraoral photographs, which achieves performance comparable to state-of-the-ar generalist models within its niche. This paradigm shift from generalist to specialist models is now clearly evident across numerous medical fields, from oncology to pathology (Qiu et al., 2023; Sarabadani al., 2025; Yang et al., 2025; 2024; Barrit et al., 2024; Mo et al., 2025; Deng et al., 2024; Xue et al., 2024; Bhaumik et al., 2023; Na, 2024; Guo et al., 2025b).
Foundational Datasets and the Cognitive Gap. Progress in Al is fundamentally tied to the quality of training data. Foundational datasets like MIMIC-CXR (Johnson al., 2019) and CheXpert (Irvin et al.. 2019) have been instrumental for tasks like chest radiograph classification. Moving up in complexity are datasets for interactive Visual Question Answering (VQA). For instance, VQA-RAD (Lau et al., 2018) () was manually constructed by clinicians asking naturally occurring questions about radiology images, representing step toward more dynamic reasoning. More recently, large-scale efforts like MedTTrinty-25M (Xie et al., 2024b) have emerged, providing over 25 million images with multi-granular annotations to support a wide range of tasks.
Within the spine domain itself, public datasets have primarily supported foundational computer vision tasks. As summarized in Table I, existing datasets largely focus on singlc-modality perception tasks. For example, the RSNA LumbarDISC dataset (Richards et al., 2025) provides large-scale MRI benchmark but is limited to the classification of stenosis severity grades. Similarly, BUU-LSPINE (Klinwichit et al., 2023) focuses on spondylolisthesis detection in X-rays, while the VerSe 2020 (Liebl et al., 2021) and Lumbar Spine MRI (van der Graaf et al., 2024) datasets provide voxel-level masks for segmentation tasks in CT and MRI, respectively.
However, these resources are primarily designed to support lower-level cognitive tasks like perception ("Where is the 1.4 vertebra?") or classification ("Is fracture present?"). They lack the multimodal integration and instructiin-followiig structure required to train models for the highest level of clinical cognition: synthesizing multimodal information into comprehensive diagnosis and treatment plan. This reveals a crucial gap between existing data paradigms and the needs of clinical practice-- gap our work aims to fill by introducing the first large-scale instruction-tuns -tuniinn-ttnngggg corpus designed for full-spectrum clinical reasoning.
AT in Spine Analysis: From Tools to Collaborators. Prior Al applications in spine analysis have focused on discrete tasks, creating valuable "tools" rather than "collaborators. These include automated vertebral segmentation and the measurement of spinal parameters (Lee et al., 2024a; Ibrahim et al., 2025a). While useful for improving efficiency, these tools perform isolated tasks, leaving the cognitive burden of synthesis and planning to the human clinician (Nath et al., 2025b). Our work directly addresses these gaps. By creating SpineMed-450k, large-scale dataset derived from clinical workflows, and SeineBench, a benchmark focused on -wwre, multimodal reasoning, we provide the infrastructure build and evaluate AI systems that can function as true clinical collaborators in the complex domain of spine surgery.
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D PERFORMANCE COMPARISON ON MEDICAL REPORT GENERATION SUBTASKS
Table 9: LVLM performance comparison on medical report generation subtasks: Imaging Report
(IR), Diagnosis (DGN), Patient Guidance (PG), Evidence- Based Plan (EBP), Technical Feasibility
(TF), Risk Prognosis Management (RPM), Coverage (COV), Relevance (REL), Granularity (GRA),
Explanation (EXP).
Model
DGN
PG
EBP
TF
RPM
COV
REL
GRA
EXP
Proprictory LVLMS
GPT-S
4,54
4.51
4.62
4.41
4.56
4,69
4.58
4,66
4,74
4,60
4.39
4.32
4.30
4.40
4.43
4.34
4:45
4.50
4.39
Demini-2..:PP%
4.55
4.51
4.79
4.60
4.64
4.79
4.69
4.83
4.84
4.80
Claude-4
3.96
4.08
4.41
3.76
3.94
4.44
4.30
4.58
4.62
4.16
GPT-4o
3.16
3.03
3.30
3,07
2.80
3.30
3.35
4,30
2.92
3.25
GPT-5-mini
4.55
4,48
4.62
4.47
4.71
4.98
4.66
4.87
4.90
4,67
Gemini-2.5-Flam
Open-sourze LVLMS (0B)
ULM-4.5V
3.85
3.78
4.12
3.77
3.59
4,05
4.26
4.63
4.23
4.09
Qwen2.5. VLB
2,98
LinShu-32B
3.05
3.05
3.22
3.44
3.04
3.49
3.21
4.34
2.90
3.44
Modgemma-27B
3.49
4.14
3.56
3.32
3.26
3.48
4.29
3.51
3.32
Open-sourze IVLMS (08))
HuxTuoGPT-7B 7B
2.42
2.42
2.91
2.76
2.77
3.37
2.77
3.50
2.57
2.63
Owen2.5-V1L-78
2.82
2.77
Ours
4.15
4.10
4.71
4.27
4.25
4.54
4.51
4.81
4.58
4.53
E PICTURE CONTEXT MATCHING ALGORITHM
The following algorithm processes Markdown files to extract image information and generate struc- tured metadata ISON format through parallel processing.
Infiolizzaion
Secalve cow.rooiieeppopomooooooooom paarmerr aupal
Processing Logic (Paralle))
Branch A Fasse idown Contert
Branch B: Scan Physical Fites
parse
"mny"" Find "inge" folder
Obaa de content
Find dr books
Check
trage
Recurdlry find Image file of phosica
mage souron caplion
Generate image data mep
lookup
Data Integration and Output
Fermat dell JSpn
Merge moadls from brenchee
Nerate thruuh physical image potho
Add whoaki patts caption hall and
Add sbsokte
Write JSON to Eie
Figure 6: picture context matching algorithm
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F DETAILED DISTRIBUTION ANALYSIS OF SPINEBENCH
To further validate the clinical representativeness and unbiased nature of SpineBench, we provide a comprehensive statistical breakdown of the testing QA set (487 items with specific subspecialty tags). These statistics demonstrate that SpineBench achieves rigorous coverage across diverse pathologies and data sources.
F.1 DISEASE SUBSPECIALTY DISTRIBUTION
As shown in Table 10, the benchmark covers 14 distinct spinal subspecialties. Notably, high-stakes and complex conditions such as Tumor and Osteomyelitis (16.72%) and Complications (10.8%%) are heavily represented, ensuring that the evaluation reflects performance critical clinical challenges rather than being dominated by common degenerative cases.
Table 10: Detailed Distribution Spine Subspecialties in SpineBench
Rank
Subspecialty
Count
Percentage
Tumor and Osteomyelitis
96
16.72%
2
Complications
62
10.80%
3
Dysplastic Congenital Deformities
8.54%
4
Idiopathic Scoliosis
47
8.19%
5
The Thoracic and Lumbar Degenerative Spine
5.92% 34
6
General Considerations
33
5.75%
7
Kyphosis and Postlaminectomy Deformitics
5.40%
Anatomic Approaches
5.40%
9
Adult Spinal Deformity
26
4.53%
10
Spondylolisthesis
23
4.01% %
11
Trauma
23
4.01%
Neuromuscular Spine Deformity
12
2.09%
13
Biomechanics
11
1.92%
14
The Cervical Degenerative Spine
1.57% 9
Total
All Subspeciallies
487
100.00%
F.2 DATA SOURCE DIVERSITY
Table 11 illustrates the provenance of the testing data. The dataset maintains rigorous balance between Academic Resources (45.8%), which ensure theoretical precision, and Real-world Clinical Data (54.2%), which test practical diagnostic robustness. Crucially, the Hospital Databases (33.5%) consist of private, internal cases that ensure zero-leakage evaluation environment.
Table 11: Detailed Source Distribution of SpineBench QAs
Category
Specific Composition
Count
Evaluation Goal
Textbooks
Standard Medical Textbooks
203
41.7%
Theoretical Foundation
Hospital Databases
Proprietary data related to pri- vacy
163
33.5%
Surgical Precision
Leakage
Clinical Case Reports
Real-world Case Reports
101
20.7%
Complex Clinical Scenarios
Literature
Academic Papers Medical Exam Questions
17
3.5%
Cutting-egge Knowledge
Question Bank
3
0.6%
Standardized Logic
Total
487
100
%
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G QUANTITATIVE COMPARISON OF SPINEGPT WITH GPT-40
Clinical Case Comparison: SpincGPT va CharGiPT-to Analysi Adolescent Idlupathic Scolioois
PATIENT
STUDINN
SperC www.arroopeaaattnntnoo
Parper
Leree ERACEIO
14
SLBAAOON ERCLATEER
VETITATTE COMPAEROS ECTBURT
Figure 7: Comparative analysis of medical report generation capabilities between SpineGPT (Ours) and ChatGPT-40 (general-purposs AI) for an adolescent idiopathic scoliosis case. The comparison demonstrates significant differences in diagnostic depth, clinical reasoning, and treatment planning specificity. SpineGPT provides 72protocols, while ChatGPT-40 offers basic diagnostic and treatment recommendations suitable for general medical documentation.
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Complete Medicul Report: Adolescent Idiopathic Scoliosis Analysis
Structured Imaging Report
Hotalty
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polld o cinar frgjocess vtick oscnal ark offecte ploceeee arrpas
w- phatigg Norgens eu medioal andl El
dig sanc deguct mn en the deyiom we sargina cencer
Diagnosis
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ass alla and aetuu
3. Sceedaa Blgges
Insicral Entager opas bibde ar comnleeed cimmorly trigetion gioe divac efie seee and proeee ochhiingg
Cospolial Scalicee ta au ausersti
homeerdine anogranachhss
uounday Lazaupaa Batelegg: Balid au senal uloal deaaca upparsace
III. Treatmeat Recommendiatioss
Parkent- Recommendations
Hon
For encitie
Cossmatte Tranmet Lnumenae
pancs artil ric operialy what they prggoen ay foots backy, natoeggoo cptae tratng phottcal heapp as lolggr officte snmopiina
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Phydrian- Contened Recommendotions
prawar kode
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Crackoice Posteas lavie dares ia segiod io ceiigg aliwn umave prised La
IV. Risk Prognosis Asseoment
Segiol Kaik
Faicaial intuli
baockig
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lealepp
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V. Management of Potential Post-operative Isones
Farernll tuee
Bed
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Aro mambem,
Inrmumettaii Jalans Fondeantrads
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couorac
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Rationale & Disclaimer
Uigaestic Summnary:
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provel ansian ua
Figure 8: Our model's medical report output for adolescent idiopathic scoliosis, featuring six-section structured format: imaging findings, AI diagnosis, treatment recommendations, risk assessment, post-operative management, and clinical rationale.
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Nocasstyy Tei Published as a conference paper at ICLR 2026
Analysis
GPT-4o Medical Report: Adolescent Idiopathic Scolfosis Analysis
Structured Imaging Report
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III. Treatment Recommendations
A. Padieni Contered Recommaddatiss
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IV. Risk & Pregnosis Amessment
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Management of Potential Post-sgerathe Issmes
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VI. Rationale & Divelaimer
Biggnast Rationale Senunary:
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diqurtik
Dicchieer:
Alggmentd Jalgneic.
Figure 9: ChatGPT-4o generated medical report for adolescent idiopathic scoliosis, showing general- purpose Al's approach to clinical documentation with basic diagnostic and treatment recommenda- tions.
H PROMPTS
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Criteria for Assessing Dimensional Quality in Reports
I. Role and Core Task
You will act as top-tier clinical medical expert and AT evaluator (L..M-Jugee.. Your core task is to rigorously compare the [LLM Generated Answer] provided below with the [Standard Answer] Based on comprehensive and detailed scoring rubrie, you will systematically evaluate the [L.LM Generated AnswerJ's performance against the [Standard Answer] across multiple dimensions. including accuracy, completeness, logical coherence, readability, and clinical utility. Finally, you will output your evaluation in the specified simple format.
II. Inputs for Evaluation
1. [Standard Answer] (Golden Answer)
[Please paste the ideal, golden standard answer here]
2. [LLM Generated Answer]
[Please paste the Al-generated answer that requires evaluation here]
I11. Evaluation Instructions Scoring Rubric
Please score the [LLM Generated Answer] for each dimension below, strictly based on its comparison with the [Standard Answer].
Please note: Scores MUST be continuous values (e.g., 3.5, 4.2, 4.7) more precisely reflect the subtle differences in the evaluation results between the integer standards. The integer scores (1-5 points) in the rubric should serve as the primary anchors for your scoring, but you should use decimal precision to capture nuanced differences. For example:
If performance slightly above "Good" (4 pts) but not quite "Excellent" pts), 4.6.
like 4.3
If performance has minor gaps compared to standard, use scores like 3.8 or 4.2. Avoid whole numbers unless the performance exactly matches the integer anchor description.
Structured Imaging Report
pts (Excellent/ On Par): On par with the [Standard Answer], accurately descrihes all key imaging findings. correctly explains their clinical significance, and includes quantitative descriptions. 4 pts (Good Minor Gaps): The description of major findings is correct, but it lacks some of the quantitative details present in the [Standard Answer].
pts (Fair Clear Gaps): The description is generally correct, but the explanation of clinical significance is clearly less sufficient or in-depth than the [Standard Answer].
2 pts (Poor/ / Serious Deficiencies): Omits or incorrectly describes key findings that are mentioned in the [Standard Answer).
1 pt (Unacceptable/ Completely Wrong): Seriously misinterprets the imaging, with key conclusions that contradict [Standard Answer].
2. Al-Assisted Diagnosis
5 pts (Excellent/ On Par): On par with the [Standard Answer]. the primary diagnosis is completely correct, secondary diagnoses are reasonably listed, and key differential diagnoses are correctly ruled out.
4 pts (Good / Minor Gaps): The primary diagnosis is correct, but the list of secondary diagnoses is less complete than in the [Standard Answer].
3 pts (Fair/ Clear Gaps): The primary diagnosis is correct but omits important differential diagnoses that are mentioned in [Standard Answer].
2 pts (Poor / Serious Deficiencies): The primary diagnosis is partially incorrect or omits key components present in the [Standard Answer).
I (Unacceptable / Completely Wrong): The diagnosis is completely wrong or misses tife-threat condition.
Treatment Recommendations
3.1 Patient-Oriented Advice
5 pts (Excellent/ On Par): On par with the [Standard Answer], the language is extremely colloquial and easy understand, the information is completely accurate, the structure is clear, and it is highly effective reassuring the patient.
Figure 10: Criteria for Assessing Dimensional Quality in Reports
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Criteria for Assessing Dimensional Quality in Reports
4 pts (Good / Minor Gaps):
The language is easy to understand and the core information is accurate, but the level empathy or nuance is slightly inferior to the [Standard Answer].
3 pts (Fair/ Clear Gaps): The language is generally understandable but contains unexplained jargon, the information is mostly correct but vague, and it clearly lacks the empathy shown in the [Standard Answer].
pts (Poor/ Serious Deficiencies): The language is obscure and jargon-heavy. the information contains errors or critical omissions, and it likely to cause patient anxicty. I pt (Unacceptable / Completely Wrong): The communication contains serious errors, is misleading, or provides harmful information, making completely unacceptable.
3.2 Treatment Plan & Evidence-BBe eereeneeeeeedddd Consistency
5 pts (Excellent / On Par): The plan's rationale, individualization, and discussion of wvidence-based support are all on par with the depth and breadth of the [Standard Answer].
4 pts (Good/ Minor Gaps): The core plan is reasonable, but the discussion of the evidence base less detailed than the [Standard Answer].
pts (Fair/ Clear Gaps): The plan is generally reasonable but lacks the individualized adjustments highlighted in the [Standard Answer].
2 pts (Poor/ / Serious Deficiencies): Parts of the plan are inconsistent with clinical guidelines, making its rationale far weaker than the [Standard Answer]'s.
1 pt (Unacceptable/ Completely Wrong): The plan clearly conflicts with evidence-based medicine and is diametrically opposed to the recommendations in the [Standard Answer].
3.3 Surgical/ecchical Details & Feasibility
pts (Excellent/ / On Par): The explanation surgical goals, technical details, preventive measures, and backup plans is comparable completeness and professionalism to the [Standard Answer]. 4 pts (Good / Minor Gaps): Covers the main technical details, but its consideration of complication prevention is less thorough than the [Standard Answer]'s. s.
3 pts (Fair / Clear Gaps): The description of details is overly general and lacks the specificity and feasibility assessment present the [Standard Answer].
2 pts (Poor / Serious Deficiencies): Omits key technical details mentioned in the [Standard Answer). making its fcasibility questionable.
I pt (Unacceptable/ Completely Wrong): The technical details are infeasible or pose safety risk.
Risk, Prognosis Post-Op Management
5 pts (Excellent / On Par): Provides a perioperative management plan, follow-up schedule, and strategy for potential issues that is as systematic, complete, and forward-thinking as the [Standard Answer].
pts (Good Minor Gaps): Covers the main measures but less systematic or detailed certain aspects compared to the [Standard Answer].
3 pts (Fair/ / Clear Gaps): Mentions basic safety measures but lacks the systematic and structured approach demonstrated in the [Standard Answer].
2 pts (Poor/ / Serious Deficiencies): Omits important safety protocols that are emphasized in the [Standard Answer].
1 pt (Unacceptable/ Completely Wrong): Seriously neglects safety, contradicting the patient-centric principles of the (Standard Answer].
Theoretical Basis & Disclaimer (EVA 4D Evaluation)
5.1 Coverage
5 pts (Excellent/ / On Par): On par with the [Standard Answer), accurately identifies and explains all key pieces of evidence with omissions.
4 pts (Good/ Minor Gaps): Covers most key evidence may omit one non-critical element that was included in the [Standard Answer].
3 pts (Fair/ / Clear Gaps): Covers the main evidence but omits one key element or two minor elements present in the [Standard Answer].
2 pts (Poor/ Serious Deficiencies): Covers only a small amount of evidence, and the chain of reasoning is far less complete than the [Standard Answer]'s.
1 pt (Unacceptable Completely Wrong): Fails to cover any key evidence, or the evidence cited contradicts the factual basis of the [Standard Answer].
Figure 11: Criteria for Assessing Dimensional Quality in Reports
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Criteria for Assessing Dimensional Quality in Reports
5.2 Relevance
5 pts (Excellent/ / On Par): On par with the [Standard Answer], all discussion is tightly focused on the core diagnosis and decision, with irrelevant content.
4 pts (Good Minor Gaps): The main content is relevant, but includes minor redundant information not found in the focused [Standard Answer].
3 pts (Fair/ / Clear Gaps): The discussion mixes relevant and irrelevant information, diluting the focus compared to the [Standard Answer].
pts (Poor/ Serious Deficiencies): The bulk of the discussion is weakly linked to the final decision, and the focus is misplaced.
1 pt (Unacceptable/ Completely Wrong): The discussion is entirely irrelevant to the diagnosis or is based on incorrect assumptions.
5.3 Granularity
5 pts (Excellent/ / On Par): On par with the [Standard AnswerJ. provides precise, quantitative details sufficient to support in-depth clinical judgment.
4 pts (Good / Minor Gaps): Provides key specific information, but the level of detail in some areas is as deep
the [Standard Answer].
3 pts (Fair/ Clear Gaps): The information is overly general and lacks the distinguishing details found in the [Standard Answer].
2 pts (Poor/ / Serious Deficiencies): Uses highly generalized language, providing far less informational value than the [Standard Answer].
I pt (Unacceptable Completely Wrong): Contains only conclusions with no supporting details, or the details are incorrect.
5.4 Explanation
5 pts (Excellent / On Par): On par with the [Standard Answer], the chain of reasoning clear, complete, and seamless, with parts logically supporting the conclusion.
4 pts (Good Minor Gaps): The overall logic is coherent, but the reasoning for a specific step is slightly less clear or direct than in the [Standard Answer].
pronounced 2 pts (Poor Serious Deficiencies): The reasoning contains clear contradictions, or the conclusion does not match the provided evidence.
3 pts (Fair/ Clear Gaps): The chain of reasoning has logical gaps jumps that than in the [Standard Answer].
I pt (Unacceptable Completely Wrong): The reasoning is fatally flawed or directly contradicts the conclusion.
IV. Required Output Format
Please strictly follow this simple format. Each line should contain exactly one score and justification:
IMAGING REPORT: [Score] [Justification]
DIAGNOSIS: [Score] [Justification]
PATIENT GUIDANCE NCE: [Score] [Justification]
EVIDENCE BASED PI.AN: [Score] |
HASED__PLANS
[Justification]
TECHNICAL. FRASIBILITYY [Score] [Justification]
RISK PROGNOSIS: [Score] [Justification]
COVERAGE: [Score] [Justification]
RELEVANCE; [Score] [Justification]
GRANULARITY: [Score] [Justificatiinn
EXPLANATION: [Score] [Justification]
Example:
IMAGING REPORT: 4.2 The report accurately describes key findings but lacks some
quantitative details.
DIAGNOSIS: 3.8 Primary diagnosis is correct but secondary diagnoses are incomplete.
Figure 12: Criteria for Assessing Dimensional Quality in Reports
27

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Published as a conference paper at ICLR 2026
Orthopedic Category Classification Prompt
Classify the orthopedic question into ONE category. Answer ONLY the category name.
Question: [question] Answer: (answer) Categories:
Spine Surgery Conditions, injuries, and surgeries related to the spinc
Foot and Ankle Surgery Conditions, injuries, and surgeries related the foot and ankle
Orthopedic Trauma Fractures, dislocations, and other acute injuries
Hand and Upper Extremity Surgery Conditions, injuries, and surgeries related to the hand, wrist, elbow, and shoulder
Musculoskeletal Oncology Bone and soft tissue tumors
Orthopedic Sports Medicine Sports-related injuries, arthroscopic surgery
Adult Joint Reconstruction Arthritis, joint replacement surgery (c.g., hip, kncc)
ANSWER WITH THE EXACT NAME: "Spine Surgery". "Foot and Ankle Surgery". "Orthopedic Trauma", "Hand and Upper Extremity Surgery", 'Musculoskelctal Oncology", "Orthopedic Sports Medicine", "Adult Joint Reconstruction"
Figure 13: Prompt for Orthopedic Category Classification
Spine Category Classification Prompt
Classify spine surgery question into ONE category. Answer ONLY category name.
Question: (question) Answer: (answer) Categories:
General Considerations Basic spine anatomy, evaluation, imaging
Biomechanics Spine mechanics, forces, stability
Anatomic Approaches Surgical approaches, exposure
The Cervical Degenerative Spine Cervical disc, stenosis, anterior cervical discectomy and fusion (ACDF)
The Thoracic and Lumbar Degenerative Spine Lumbar disc, stenosis, fusion
Spondylolisthesis Spondylolisthesis, pars interarticularis defect
Idiopathic Scoliosis Adolescent idiopathic scoliosis, curves
Adult Spinal Deformity Adult scoliosis, sagittal balance
Dysplastic and Congenital Deformities Dysplastic and congenital deformities
Neuromuscular Spine Deformity Neuromuscular spinal deformity
Kyphosis and Postlaminectomy Deformitics Kyphosis, post-laminectomy deformities
Trauma- Spine fractures, spinal cord injury
Tumor and Osteomyelitis Spine tumors. infections
Complications- Surgical complications, internal fixation failure
ANSWER WITH THE EXACT NAME: "General Considerations". "Biomechanies", "Anatomic Approaches", "The Cervical Degenerative Spine", "The Thoracic and Lumbar Degenerative Spine". "Spondylolisthesis"" "Idiopathic Scoliosis". "Adult Spinal Deformity". "Dysplastic and Congenital Deformities". "Ncuromuscular Spine Deformity". "Kyphosis and Postlaminectomy Deformities". "Trauma", "Tumor and Osteomyelitis". "Complications"
Figure 14: Prompt for Spine Category Classification
28

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Published as a conference paper at ICLR 2026
Generating Medical Q&A for Fine-Tuning Prompt
"You are a senior clinical medical educator. Please carefully read the provided medical textbook content below and generate muulipple high-quality open-ended question-answer pairs* based on the core knowledge points, all for fine-tuning large language models. Each Q&A should be self-contained and completely independent."
"*#Strict Requirements:#*"
"1. C*Comllete Independence**: Each question and answer must constitute complete knowledge unit that can be understood without any external background materials."
"2. **Prohibited Referential Terms*"; Strietly prohibit using terms like this guide, the study, the above materials, This article, the reportör any other terms that refer to the original text in questions answers.
"3. "*Clinical Depth Requirements**; Questions should reflect real clinical scenarios, testing deep understanding and clinical thinking rather than simple yes/no questions."
"4. **Open- Ended Design*#: Questions should encourage detailed analysis, requiring comprehensive, structured answers that demonstrate clinical reasoning processes." "
"5. ** **nsweer Completeness*e; Answers must detailed and comprehensive. including analysis
process, reasoning logic, and final conclusions."
"6. **Question Type Diversity**: Should cover multiple dimensions including pathological mechanism explanation, diagnostic thinking analysis, treatment plan design, complication prevention strategies, ctc."
"**Question Quantity Requirements:***
Generate appropriate number of questions based on text length"
Short text (1-2 paragraphs): Generate 2-3 questions"
Medium text (3-5 paragraphs): Generate 3-5 questions"
Long text (6+ paragraphs): Generate 5-8 questions" O*tuut Format Requirements:
"Strictly follow the XML format below, each textbook page can generate multiple questions:" ""xmll
"<problem=<(penn-nne question stem]//problems"
"canswer>[Detailed open-ended answer question 1, including analysis process conclu- sions]</answer.
question 2 stem]//problem>>" (Detalle open-ended answer for question 2, including analysis process and conclu- sions]</answers
Sprobllmm(Openn-oeee ended question steem//problem>"
(Detalld open-ended answer for question 3, including analysis process and conclu- s/anse<<answer>"
"4-4 Important N
"1. Strictly follow the format"
"2. Question stems should be clear and specific, encouraging deep thinking, avoiding simple yes/no questions"
"3. Answers should be comprehensive detailed, including analysis process, reasoning logic, and final conclusions"
"4. Output only XML objects, no additional explanatory text"
"5. Each question should be independent and complete, not dependent on other questions or external materials"
"6. Answers should demonstrate depth medical professional knowledge and the logic of clinical thinking"
Te*tebbook "cootet""""onten"
"Please generate high-quality medical open- question question-answer answer pairs based the above textbook content."
Figure 15: Prompt for Generating Medical Q&A for Fine-Tuning
29

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Published as a conference paper at ICLR 2026
Generating Medical MCQs for Fine- Tine-Tuning Prompt
"You are a senior clinical medical educator and examination expert. Please carefully read the provided medical textbook content below and generate **multiple high-quality multiple-choice questions with answers*s based the core knowledge points, all for fine-tuning large language models. Each Q&A should be self-contained completely independent."
"**Strict Requirements:
"1. **Compltte Independence**; Each question and options must constitute complete knowledge unit that can he understood without any external background materials."
"2. **Prohibited Referential *erms**; Strictly prohibit using terms like this guide, the study, ihe above materials; this article; the reportör any other terms that refer to the original text in questions or options." "3. **Clinical Depth Requirements** Questions should reflect real clinical scenarios, testing deep understanding and clinical judgment rather than simple memorization."
"4. **Option Design**; Each question must include options (A, B, C, D), with correct high-quality distractors. Distractors should be based on common clinical misconceptions related concepts."
and
"5. **Question Type Diversity** * Should cover multiple dimensions including diagnostic reasoning, treatment selection, mechanism explanation, differential diagnosis, complication prevention, etc."
"4** "u=Quetion Quantity Requirements:
Generate appropriate number questions based on text length"
Short text (1-2 paragraphs): Generate 2-3 questions"
Medium text (3-5 paragraphs): Generate 4-6 questions"
Long text (6+ paragraphs): Generate 7-10 questions"
"** Output Format Requirements:
"Strictly follow the XML format below, each textbook page can generate multiple questions:" ""xml"
"<problem=<QQesstinn stem] A. [Option A] B. [Option BJ C. [Option [Oppiio D}</problem>> option s/anse\</answer>""
"<answer>(Question 1 correct
"<problem==(Question stem] [Option A] [Option B] C. [Option C) D. [Option DJ</problem>>
(<unswer=(Queettonn 2 correct answer option letter]<<ansser>"
<problem==(Quesionn stem] A. [Option A] B. [Option BJ C. [Option CJ D. [Option DJ</problem>>
<<answer>(Questinn 3 correct answer option letter]</answer>"
"4-4 *Imoorattt N
"1. Strictly follow the format"
"2. Question stems should be clear and specific, testing deep understanding and clinical judgment"
"3. Options should be reasonably designed, including correct answers and high-quality distractors"
Figure 16: Prompt for Generating Medical MCQs for Fine- -unnng
30

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Published as a conference paper at ICLR 2026
Generating Context-Localize Localized Multimodal Q&A Prompt
are senior clinical medical educator. Based provided image information, caption, and context, precisely locate the image's position in the context and generate high-quality open-ended questions and answers."
"**Core Task: Multimodal Understanding and Precise Localization Open-Ended Q&A**" Step Multimodal Information Understanding***
"1. mmlmage Understanding**; Analyze the specific medical content shown the image (anatomical structures, pathological manifestations, surgical procedures, imaging features, instrument usage, etc.)" "2. **Caption Understanding**; Identify figure numbers, positions, operational steps, key information mentioned in the caption"
"3. **Context Understanding**:; Analyze medical knowledge points, operational procedures, and
clinical key points in the preceding and following text"
"4. **Position Localization**; Precisely locate the image's specific position and role in the context" "**Step 2: Precise Position Localization***
Matching**: " If the caption contains a figure number (c.g., Figure 12.1), 1), find the corresponding figure reference in the context"
"1. **CaaaioonCoomeeeee
If the caption describes operational steps, locate the corresponding operational description in the context"
If caption describes anatomical structures, find related anatomical descriptions in the context"
"2. **Context Position Analysis**;"
Analyze preceding text: background information, preparation steps, and related concepts before the image appears"
Analyze following text: operational steps, precautions, and clinical significance after the image shown"
Determine the image's specific role in the entire process"
"** 3: Generate
Q&A Based on Precisely Located Content
"Must generate open-ended questions and answers based precisely located medical knowledge points:"
Deeply analyze the relationship between located content and the image"
Generate open-ended questions based on precisely located content" Ensure questions are highly relevant to both image content and context"
If unable precisely locate suitable content, skip this question" "** Step High-Quality Open-Ended Q&A Design**" "Based on precisely located content, generate open-ended questions and answers with the following characteristics."
"**Q&A Design Principles:
"1. 4* Multimodal Relevance**; Questions must he relevant image content, caption information, and context content simultancously"
"2. **Clinical Orientation**; *+; Questions should be based on real clinical scenarios, testing clinical
thinking and decision abilities"
"3. =nOpen-Ended Design* Encourage deep thinking, avoid yes/no questions, require detailed
analysis"
"4. **Position Precision Questions should be based on the image's 's precise location the context"
"5. ""Prohibited Referential * Strietly prohibit using terms like according to the context, this guide; 7he study; the above materials; this article; The reportör any other terms that refer to the original text in questions answers"
"6. ** Answer Design*s."
Figure 17: Prompt for Generating Loctaxe-Localized Multimodal Q&A
31

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Published as a conference paper at ICLR 2026
Generating Context-Localized Multimodal Q&A Prompt
Answers must be based precisely located content"
Cover relevant medical knowledge and clinical considcrations"
Reflect clinical thinking and decision-making process"
"7.
**Qussion Type Priority**."
Diagnostic analysis questions (in-depth analysis based imaging findings, clinical symptoms, etc.)
Treatment decision questions (detailed analysis of surgical indications, treatment plan selection, etc.)"
Mechanism explanation questions (in-depth explanation of anatomical nuatomical-physiooogicaa. relationships,
pathological mechanisms, etc.)"
Technical operation questions (detailed explanation of surgical steps, instrument usage, etc.)"
Risk assessment questions (comprehensive analysis of complication prevention, management strategies, etc.)*
"+**utput Format Requirements:#*"
"Strictly follow the following XML format, each image can generate multiple different Q&A pairs."
""'xml"
"<problem><tinae>>
n First open-ended question stem] /</problem>"
<aansee><First detailed open-ended answer]</answer>"
"<problem><cinae>>
n[Second open-ended question stemm</problem>"
"<answer>[Second answer, directly answering the question)<answer>"
"<problem><image>>
Third open-ended question stem)</probleens
Canswer>[Thire answer, directly answering the q
"** Immporttnt Notes:
"1. Strictly follow the XML format"
"2. Question stems should be clear and specific, encouraging deep thinking, avoiding simple yes/no questions"
"3. Answers should be comprehensive detailed, including analysis process, reasoning logic, and final conclusions"
"4. If unable to precisely locate relevant content, do not generate questions"
"5. Output only complete XML object"
"6. Strictly prohibit using referential terms"
"7. **Image Reference Standards* When referencing images questions, use general terms like
äs shown in the image; The image displays, imaging findingsêtc., strictly prohibit using specific figure numbers (e.g.. Figure 10.8, Figure 12.1, etc.)"
Proccsssing #orkflow:**"
"1. Analyze the image caption understand the specific medical content shown"
"2. Precisely locate medical knowledge points in the context related to the caption"
"3. Determine the image's specifie position and role the context"
"4. precise localization is successful and content is suitable for questions, generate open-ended Q&A
based on located content"
"5. If unable to precisely locate or content is not suitable for questions, do not generate questions"
"6. Ensure questions have clinical value and educational significance"
"** Proviiddd Information: ***
"Image Caption: caption"
"Context Information: context"
"Please precisely locate the image's 's position in the context and generate high-quality medical open- ended questions and answers. If unable to precisely locate suitable content, do not generate questions. Strictly follow the specified XML. format."
Figure 18: Prompt for Generating Context-Localize Multimodal Q&A
32

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Published as a conference paper at ICLR 2026
Generating Context-Localized Multimodal MCQs Prompt
"You senior clinical medical educator and examination expert. Your task is to precisely locate the image's position in the context based the provided image information, caption, and context, then generate high-quality multiple-choice questions." "
"***Cre Task: Multimodal Understanding and Precise Localization Question Generation "4*Step 1: Multimodal Information Understanding
"1. "* ""Image Understanding** Analyze the specific medical content shown in the image (anatomical structures, pathological manifestations, surgical procedures, imaging features, instrument usage, etc.)" "2. **Caption Understanding" **; Identify figure numbers, positions, operational steps, or key information mentioned in the caption"
"3. **Context Understanding*** Analyze medical knowledge points, operational procedures, and clinical key points the preceding and following text"
"4. **Position Localization Precisely locate the image's specific position and role in context"
"**Step 2: Precise Position Localization
"1. **Cap**on Croptioo-Cooteet Matching**:
If the caption contains figure number (c.g., Figure 12.1), find the corresponding figure reference in the context"
the caption describes operational steps, locate the corresponding operational description in the context"
If the caption describes anatomical structures, find related anatomical descriptions in the context"
"2. **Context Position Analysis**."
Analyze preceding text: background information, preparation steps, and related concepts before the image appears"
Analyze following text: operational steps, precautions, and clinical significance after the image is shown"
Determine the image's specific role in the entire process"
Step 3: Generate Questions Based on Precisely Located *ontent**"
"Must generate clinical multiple-choice questions based on precisely located medical knowledge points:"
Deeply amalyze the relationship between located content and the image"
Generate multiple-choice questions based on precisely located content" Ensure questions are highly relevant to both image content and context" If unable to precisely locate suitable content, skip this question" Step 4: High- Suah-uulitt Multiple-Chol Choice Question Design " "Based precisely located content, generate clinical multiple-choice questions with the following characteristics."
"++Q&A Design Principles:
"1. ** Multimodal **levance**; Questions must be relevant to image content, caption information, and
context content simultaneously"
"2. **Clinical *+ ++; Questions should based on real clinical scenarios, testing clinical
thinking and Docisiion-maknn abilities"
"3. *=Multple-CChoico Design**; Provide multiple options to test deep understanding and clinical judgment"
"4. **Position +: Questions should be based the image's 's precise location in the context"
"5. s*Prohibited Referential *erms**; Strietly prohibit using terms like according to context, this guide, The study; the above materials; this article, The reportör any other terms that refer to the original text in questions or options"
Figure 19: Prompt for Generating Context-Localized Multimodal MCQs
33

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Published as a conference paper at ICLR 2026
Generating Context-Loccllzed Multimodal MCQs Prompt
"6. **Option Design **"" **
Correct answer must he hased on precisely located content"
Distractors should he based on common clinical misconceptions or related but inaccurate concepts"
All options should have clinical plausibility"
"7. **eustion Type Priority
Diagnostic analysis questions (in-depth analysis based imaging findings, clinical symptoms, etc.)"
Treatment decision questions (detailed analysis of surgical indications, treatment plan selection, etc.)"
Mcchanism explanation questions (in-depth explanation of anatomical anatomical-ppysiooleeee ppyssiooogical relationships, pathological mechanisms, etc.)"
Technical operation questions (detailed explanation surgical steps, instrument usage, etc.)"
Risk assessment questions (comprehensive analysis of complication prevention, management strategies, etc.)"
"**Output Format Requirements:***
"**Strictly follow the following XML format:*** ""xml"
"<problem><image>>
n [Question stem] A. [Option A] B. [Option B] C. [Option CJ D. [Option DJ</problems" "<arswers[Corret answer option lettse\<//nswer>"
ma.e
Important Sotes:
"1. Strictly follow the XML format"
"2. Question stems should be clear and specific, testing deep understanding and clinical judgment"
"3. Options should reasonably designed, including correct answers and distractors"
"4. If unable to precisely locate relevant content, do not generate questions"
"5. Output only one complete XML object"
"6. Strictly prohibit using referential terms"
"7. **Image Reference Slandards**, When referencing images in questions, general terms like
shown in the image; image displays, imaging findingsêtc., strictly prohibit using specific figure numbers (c.g., Figure 10.8, Figure 12.1, etc.)" "M*Prccssing Workflow:
"1. Analyze the image caption to understand the specific medical content shown"
"2. Precisely locate medical knowledge points in the context related to the caption"
"3. Determine the image's specific position and role in the context"
"4. If precise localization successful and content suitable for questions, generate multiple-choice
questionns based on located content"
"5. If unable to precisely locate or content is not suitable for questions, do not generate questions"
"6. Ensure questions have clinical value and educational significance"
PeoProvdded Information: **
"Image Caption: caption"
"Context Information: context"
"Please precisely locate the image's 's position in the context and generate high-quality medical multiple- choice questions. If unable to precisely locate suitable content, do not generate questions. Strictly follow the specified XML. format."
Figure 20: Prompt for Generating Context-Localized Multimodal MCQs
34


Reviews:
[{"review_id": "911lGdlEui", "reviewer": "Reviewer_xkSD", "summary": "Spine disorders affect 619 million people and are a leading cause of disability, which has been constrained by the absence of traceable, clinically-grounded instruction data and standardized, spine-specific benchmarks. This paper develops a clinically-grounded evaluation framework and a large-scale dataset explicitly designed for vertebral-level reasoning. Moreover, people demonstrate the effectiveness of their dataset by training a fine-tuned spine LVLM.", "questions": "1. Is the dataset and baseline model publicly available?\n2. For the dataset generation, the authors used the Expert VLM Model to generate questions. How to make sure the questions and answers are correct?  \n3. Could the authors provide the performance on other Spinal diagnosis and treatment datasets?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper focuses on spine disorders, which are a leading cause of disability. The paper is clearly presented and easy to follow.", "weaknesses": "1. Lack of novelty. This paper lacks technical contributions. The pipeline for dataset curation is a common solution, without any novel techniques or insights. \n2. Please provide a table to compare SpineMed-450k with other Spinal diagnosis and treatment datasets. \n3. Since the baseline is the contribution in this paper, please introduce it as an independent section.\n4. The SpineBench benchmark only has 487 questions and 87 report prompts, which is a small test subset, possibly limiting generalizability and statistical robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PXDpwGL0ae", "reviewer": "Reviewer_1Ksq", "summary": "This paper introduces SpineMed-450k, a large-scale dataset of 450,000+ instruction instances for vertebral-level reasoning across spine imaging modalities, and SpineBench, a clinically-grounded evaluation framework. The dataset is curated through a \"clinician-in-the-loop\" pipeline involving diverse sources (textbooks, guidelines, public datasets, ~1,000 hospital cases) with a two-stage LLM generation method. The authors evaluate multiple state-of-the-art LVLMs and present SpineGPT, a fine-tuned model that demonstrates improvements over existing models on spine-specific tasks.", "questions": "1. Will SpineMed-450k be publicly released? Open-sourcing the dataset would significantly benefit community development and enable reproducible research. Please clarify the release timeline, licensing terms, and any restrictions due to hospital case privacy considerations.\n2. SpineGPT (87.44%) underperforms Gemini-2.5-Pro (89.23%) despite being trained on SpineMed-450k. What factors contribute to this gap? Is it due to model scale (7B vs. proprietary larger model), training strategy, data quality, or fundamental architectural limitations? A deeper analysis would strengthen the paper's insights.\n3. Regarding Figure 3 and the data curation pipeline, how do you address potential OCR errors such as figure-text misalignment or incorrect caption matching during the structured information extraction process? What quality control mechanisms ensure the Picture Context Matching algorithm correctly pairs images with their contextual descriptions?\n4. In Table 5, what would be the performance if the model were trained exclusively on spine data without the general orthopedic (non-spine) data? This ablation would clarify whether the multi-stage curriculum (general → orthopedic → spine) is necessary or if direct spine-specific training suffices.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The paper is well-written with effective communication of clinical motivation, technical approach, and results. \n2. The paper presents SpineMed-450k aggregates 450k+ high-quality instruction instances from diverse sources—textbooks (377k), clinical guidelines, real hospital cases (~1,000 from 11 hospitals), and public datasets, proposes SpineBench, which offers a professionally curated, level-aware benchmark specifically designed for spine diagnosis and treatment planning.\n3. The paper systematically evaluates a dozen contemporary open-source LVLMs on SpineBench. This rigorous assessment establishes important baseline performance standards and identifies key research gaps for the community.", "weaknesses": "1. SpineGPT does not specifically leverage or optimize for the unique properties of SpineMed-450k. The training still relies heavily on generic open-source medical data in the first stage (Appendix Table 6), raising questions about whether the model fully exploits the dataset's clinically-grounded, level-aware structure.\n2. Suboptimal performance compared to state-of-the-art: Table 4 shows SpineGPT (87.44%) underperforms Gemini-2.5-Pro (89.23%) on average; Appendix Table 7 confirms this performance gap across multiple metrics; and Missing comparisons with recent specialized medical models (e.g., Baichuan-M2, MedGemini), limiting assessment of the model's competitive standing\n3. The Dataset Generation section indicates that instruction tuning data generated from open-source spine datasets (Spark, VerSe) lacks rigorous quality verification, unlike the hospital cases which underwent expert validation. This inconsistency may introduce noise and affect model reliability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "JxTynSbwmj", "reviewer": "Reviewer_8nUb", "summary": "The work presents a largest dataset explicitly designed for vertebral-level reasoning and a related benchmark, which is medically meaningful, but several important points in the manuscript remain unclear or have issues that should be addressed.", "questions": "Please answer the following questions:\n\n1. How do the authors demonstrate the importance of the level-aware design? Is there any ablation study showing that the level-aware component is necessary (for example, a performance drop when level information is removed or when levels are mixed)?\n\n2. Since many models already achieve >80, it appears that even without this work, general-domain training data is already sufficient to solve most of the tasks at a reasonably high level. In that case, how should the importance/necessity of this dataset be interpreted? Additionally, does the benchmark pose a risk of data leakage, i.e., that the evaluated models have already encountered similar data during pretraining?\n\n3. What is the “Expert LLM”? The authors should clearly define which model this refers to, how it is used in the pipeline, and what prompt format is applied. How was this Expert LLM obtained or fine-tuned?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper contributes SpineMed-450k, the largest spine diagnosis dataset, with 450,000+ multi-modal instruction instances covering diverse tasks across 14 spine conditions, filling a significant gap in specialized medical AI resources.\n\n2. The authors demonstrated genuine \"clinician-in-the-loop\" design with 17 board-certified orthopedic surgeons participating across data curation, task definition, and validation stages.\n\n3. The 10-dimensional evaluation assesses for AI-generated clinical reports not just diagnostic accuracy but also communication quality, reasoning transparency, risk assessment, and technical feasibility, better capturing what makes clinical reports actually useful in practice.\n\n4. The ablation studies convincingly show that a 7B model trained on specialized spine data substantially outperforms 72B general models, proved the data effective in the certain task.", "weaknesses": "1. The manuscript emphasizes the importance of being “level-aware,” and I agree this could be meaningful. However, in the dataset and benchmark I do not see a clear level-aware design. Some subsets are said to target certain levels, but their structure looks basically the same as the general medical data. It is not clear how “level-aware” actually affects data curation, question type, or evaluation protocol.\n\n2. Many models achieve scores above 80. With such results, the claim that current models still have serious gaps in this domain is not fully convincing. If the tasks are truly difficult and clinically important, we would normally expect lower scores or at least a stronger separation between models. This raises the concern that either the tasks may be too simple, or there may be significant data leakage in the benchmark.\n\n3. The paper mentions an “Expert LLM,” but this term is not defined. Please specify what model is used, and why it is called “expert.” Also, is this the same model as the “Expert VLM” that generates Q&A pairs, or are these different systems? This point is unclear and affects reproducibility.\n\nMinor weakness:\nThe data pipeline includes “simulated consultations,” but the benchmark tasks do not include any dialogue / consultation evaluation. Why is this step necessary? What is the purpose of generating simulated consultations if the final tasks are not dialogue-based?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Wr3PPRFNcx", "reviewer": "Reviewer_SXG5", "summary": "The authors proposed a spine dataset containing multimodal data collected from about 1000 clinical cases. Several tasks, e.g., question-answering, multi-turn consultations, and report generation, are composed based on the multimodal data. The authors also trained a VLM using both some public datasets (as general medical domain knowledge) and the data from the proposed dataset. The manuscript is overall easy to follow, but it also suffers from several critical flaws that significantly degrade the usefulness of the proposed data.", "questions": "N/A", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The proposed dataset focuses on the spine-related diseases, which are rare in the community.\n- The proposed dataset and benchmark are composed using multi-modality data, beyond the texts and images, which is closer to the problems in real-world practice.\n- The paper is overall easy to follow.", "weaknesses": "-  The presented results fail to demonstrate the advantage of the proposed model (trained with the domain data) in comparison to generalist models, as shown in Table 4. It is also a bit surprising to see that models trained on medical-domain data (e.g., Linshu and Huatuo) often perform worse than generalist models.\n-  The design of the testing set in the proposed benchmark may not evaluate \"the overall performance of AI systems in spinal diagnostic tasks,\" considering only the multiple-choice and reporting tasks are included in the testing. The mismatch between the tasks in the data composition and those in the testing is also questionable and somewhat misleading in justifying the benchmark's significance. \n- Many details of the dataset and the process of dataset composition are missing. For example, it is not clear about the number of samples and data entries included in the final testing and training set, since only multiple-choice and reporting parts are included in testing. How many cases are associated with those data? Report examples are NOT provided in Appendix B.5. \n- The dataset employed roughly 1000 cases, which is relatively small considering that the data are from 11 different hospitals and cover dozens of diseases and scenarios. The diversity of the included is therefore questionable to evaluate the overall performance in spinal diagnostic tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)