ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Performance of large language models in interpreting evidence-based clinical guidelines for lumbar disc herniation with radiculopathy.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Assessment of brachial plexus and upper-extremity peripheral nerve injuries at the anatomical level using multimodal generative models.Frontiers in surgery · 2026Article
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Abstract
objectiveLarge language models (LLMs) are increasingly used as clinical information tools; however, their ability to accurately interpret evidence-based spine guidelines remains unclear. This study compared the performance of ChatGPT-5.1, Gemini, and Perplexity in interpreting the North American Spine Society (NASS) guideline for lumbar disc herniation with radiculopathy.
methodsNineteen open-ended clinical questions derived from the NASS guideline were submitted to each LLM under standardized conditions. Responses were evaluated by two blinded clinicians using validated Likert scales for clinical accuracy (1-5), reliability, and usability (1-7). Semantic similarity to guideline-based answers was assessed using the Universal Sentence Encoder, surface-level textual similarity using ROUGE-L F1 scores, and readability using multiple established readability indices. Reference reliability was analyzed using the Reference Hallucination Score.
resultsPerplexity demonstrated significantly higher clinical accuracy (3.95 ± 0.70) compared with ChatGPT-5.1 (3.45 ± 0.68) and Gemini (3.50 ± 0.65) (p = 0.018). Reliability and usability scores were also highest for Perplexity (4.85 ± 1.05 and 4.75 ± 0.95, respectively; both p < 0.01). Semantic similarity scores were greater for Perplexity (0.71 ± 0.06) than for ChatGPT-5.1 (0.64 ± 0.07) (p < 0.001), whereas Gemini achieved the highest ROUGE-L F1 scores (0.14 ± 0.04; p < 0.001). Readability indices were comparable across models, indicating similar levels of textual complexity. ChatGPT-5.1 exhibited the highest reference hallucination (8.10 ± 2.85), while Perplexity showed the lowest (4.15 ± 2.70) (p < 0.001).
conclusionsLLMs show significant variability in guideline-based clinical reasoning. Although none should be used as independent decision-making tools, reference-oriented models may provide more reliable adjunctive support for evidence-based spine practice.
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Registered trials
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