Abstract
INTRODUCTION: While large language models (LLMs) are increasingly used in clinical decision support, their adherence to evidence-based guidelines-particularly for musculoskeletal pain management-remains understudied. METHODS: Four 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. RESULTS: The 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%). CONCLUSIONS: LLMs 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.