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LLMs in Spine Surgery: A Scoping Review Maps the Gap Between Knowledge Retrieval and Surgical Autonomy
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Lead
This scoping review evaluates Large Language Models (LLMs) in spine surgery, introducing the Vertical Integration Maturity Scale (VIMS). It reveals that while LLMs excel at guideline retrieval, they lack validated capability for autonomous, tactile surgical planning, highlighting a critical gap in prospective clinical evidence.
Key Facts
| Metric | Value | Source |
|---|---|---|
| Studies Included | 40 studies (from 351 identified) | PMID: 42686880 |
| Peak Research Area | Stage III (Evaluative Decision Logic) | PMID: 42686880 |
| Guideline Concordance | 61.1% to 88.2% (ChatGPT-4) | PMID: 42686880 |
| Complex Deformity Precision | 11.1% | PMID: 42686880 |
Why It Matters / Context
The integration of Artificial Intelligence (AI) into surgical workflows has moved rapidly from theoretical potential to practical application, particularly in orthopedic and spine surgery. However, the literature has largely fragmented, focusing on isolated tasks like radiograph reading or preoperative planning without a unified framework to assess where in the surgical workflow these models actually function.
This review is significant because it introduces the Vertical Integration Maturity Scale (VIMS), a novel two-dimensional framework that maps model maturity across five functional workflow stages. This is crucial for oncology and surgical communities because it distinguishes between “knowledge retrieval” (what the model knows) and “procedural agency” (what the model can do).
The findings suggest that current LLMs are proficient in foundational medical knowledge but fail in non-deterministic surgical scenarios. Specifically, the review identifies a “critical evidence gap” in Stage IV (Tactile Procedure Planning), where no studies have evaluated autonomous workflow agency at higher VIMS levels (4-5). This absence is likely due to the reliance on retrospective study designs using proprietary, non-version-controlled models, which limits the ability to validate inferential validity in real-world, complex surgical environments. For clinicians, this underscores that while LLMs can serve as robust decision-support tools for guideline-based care, they are not yet ready for autonomous surgical decision-making.
Details: Workflow Maturity and Performance
The review categorized 40 studies using the VIMS framework, which assesses models across five stages of the vertical spine surgery workflow. The following table summarizes the distribution of research density and key performance benchmarks identified in the study.
| VIMS Stage | Functional Focus | Research Density (n) | Key Findings / Performance |
|---|---|---|---|
| Stage I | Data Ingestion & Preprocessing | Low | Limited focus on raw data standardization. |
| Stage II | Diagnostic Interpretation | Moderate | Models show variable precision; complex cases remain challenging. |
| Stage III | Evaluative Decision Logic | High (n=15) | Peak research area. Focus on evidence-based guideline retrieval. ChatGPT-4 showed 61.1%–88.2% concordance with guidelines PMID: 42686880. |
| Stage IV | Tactile Procedure Planning | Zero (n=0) | Critical Gap. No studies evaluating autonomous workflow agency (VIMS 4-5). |
| Stage V | Intraoperative Autonomy | Zero (n=0) | Critical Gap. No evidence for real-time autonomous decision-making. |
Performance Discrepancies
The review highlights a stark contrast in model performance based on case complexity:
- Guideline Concordance: In standard evaluative tasks, ChatGPT-4 demonstrated high concordance with clinical guidelines, ranging from 61.1% to 88.2% PMID: 42686880.
- Complex Pathology: In complex spinal deformity cases, diagnostic precision dropped significantly to 11.1% PMID: 42686880. This suggests that while LLMs can retrieve and synthesize general medical knowledge, they struggle with the nuanced, multi-variable reasoning required for complex structural pathologies.
Methodological Approach
The authors utilized a systematic search across PubMed, Embase, and Scopus for peer-reviewed studies published between January 2023 and January 2026 PMID: 42686880. The selection process employed the Population, Concept, and Context (PCC) framework, focusing on generative AI applications in the clinical evaluation and surgical management of spinal pathologies. Data analysis involved independent dual-coding to ensure reliability, with inter-rater reliability calculated to validate the novel VIMS framework PMID: 42686880.
Limitations
⚠️ Critical Limitations1. Retrospective Bias: The review notes that the absence of studies in Stage IV (Tactile Procedure Planning) likely reflects a limitation of retrospective study designs. Most current research relies on proprietary, non-version-controlled models, making it difficult to validate consistent performance over time PMID: 42686880.
2. Heterogeneous Benchmarks: The inferential strength of the trends identified is limited by heterogeneous benchmarking metrics across the included studies. There is no standardized metric for evaluating “surgical reasoning” PMID: 42686880.
3. Scope: The review is specific to spine surgery. While the VIMS framework may be applicable to other surgical specialties, the evidence base is limited to spinal pathologies PMID: 42686880.
4. Temporal Scope: The search window (Jan 2023–Jan 2026) captures recent developments but may miss earlier foundational work or very recent preprints not yet indexed in the major databases PMID: 42686880.
Clinical Takeaway
Clinicians should utilize LLMs as adjunctive tools for guideline retrieval and preoperative planning (Stage III), but must not rely on them for autonomous decision-making in complex or tactile surgical scenarios (Stage IV-V) until prospective, standardized clinical trials validate their inferential validity.
Full Reference
Lui EHY, Mobbs RJ. Large language models in the vertical integration of spine surgery workflow: a scoping review. European Spine Journal. 2026. PMID: 42686880 [DOI: 10.1007/s00586-026-10348-x]