Guideline-augmented large language models for contraindication screening in interventional spine care.

BACKGROUND: Large language models (LLMs) are increasingly used for clinical decision support in spine care, but their ability to classify contraindications to interventional procedures according to safety guidelines is unknown. OBJECTIVE: This study evaluated LLM accuracy using the International Pain and Spine Intervention Society (IPSIS) Safety Practices and assessed whether retrieval-augmented generation (RAG) improves performance. METHODS: A database of 318 clinical scenarios was curated from IPSIS Safety Practice guidelines spanning 12 procedure categories (102 absolute contraindications, 57 relative contraindications, 159 matched controls). Three LLMs (GPT-5.4, Gemini 3.1 Pro, Claude Sonnet 4.6) classified each case as no contraindication, relative, or absolute under baseline and RAG conditions. McNemar tests with Bonferroni correction compared conditions within models. Between-model comparisons used Cochran's Q test with post-hoc pairwise analysis. RESULTS: Baseline accuracy ranged from 85.8% to 87.1% across models and improved to 92.5% to 98.4% with RAG (all p < 0.05). Weighted kappa improved from 0.885-0.913 to 0.949-0.990. Safety catch rates for absolute contraindications were 99.0% to 100.0% across all conditions. Relative contraindications showed the largest improvements with RAG, increasing from 42.1%-56.1% to 59.6%-93.0%. CONCLUSION: LLMs demonstrated high reliability in identifying absolute contraindications but were less consistent for relative contraindications. RAG significantly improved classification accuracy across all models, supporting further evaluation of guideline-augmented LLMs for pre-procedural safety screening.