ArticleClinical rheumatology2025
Comparative evaluation of large language models in delivering guideline-compliant recommendations for topical NSAID use in musculoskeletal pain: a multidimensional analysis.
Article in Clinical rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Guideline-augmented large language models for contraindication screening in interventional spine care.Interventional pain medicine · 2026Article
- A comparative evaluation of large language models in aligning with European Respiratory Society (ERS) guidelines for high-flow nasal cannula in acute respiratory failure.Journal of thoracic disease · 2026Article
- Applications of DeepSeek in Medicine: Bibliometric Analysis and Scoping Review.Journal of medical Internet research · 2026Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWhile large language models (LLMs) are increasingly used in clinical decision support, their adherence to evidence-based guidelines-particularly for musculoskeletal pain management-remains understudied.
methodsFour LLMs (DeepSeek-R1, ChatGPT-4o, Gemini, Grok-3) were evaluated on their responses to topical NSAID use for musculoskeletal pain through: assessments of response quality (accuracy, over-conclusiveness, supplementary information, and incompleteness), standardized readability metrics (Flesch Reading Ease, Flesch-Kincaid Grade Level), and the PEMAT-P tool to quantify actionability.
resultsThe four LLMs showed significant variability in accuracy (ANOVA p = 0.045), with Gemini scoring highest (8.33 ± 0.77) and DeepSeek-R1 lowest (7.72 ± 1.52) and in over-conclusiveness (ANOVA p = 0.025), with Grok-3 scoring lowest (4.56 ± 1.42) and ChatGPT-4o highest 6.72 ± 1.49). ChatGPT-4o provided the most supplementary content (6.94 ± 2.29, p = 0.106) and DeepSeek-R1 had the highest incompleteness (5.00 ± 2.52, p = 0.261). All models exceeded recommended readability thresholds (9th-10th grade level), and none met the actionability standard (≤ 33.5%).
conclusionsLLMs demonstrate potential as clinical aids. The comprehensive performance of Gemini and Grok is relatively favorable, yet their readability and actionability remain unsatisfactory. Future development should integrate clinician feedback and real-world validation to ensure safety. Human oversight and targeted AI training are critical for safe implementation. Key Points • The study reveals significant differences in accuracy among LLMs, highlighting inconsistencies in clinical decision support. • While all models generated readable text, the complexity remained high, potentially limiting accessibility for some patients. • Glucocorticoid use for patients in remission was more strongly associated with impaired physical function in patients aged 75-84 than in patients aged 55-74 years. • Over-conclusiveness and incomplete adherence to evidence-based guidelines underscore the necessity for human oversight and targeted AI training in clinical applications.
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