Assessing LLMs on IDSA Practice Guidelines for the Diagnosis and Treatment of Native Vertebral Osteomyelitis: A Comparison Study

Abstract

Background: Native vertebral osteomyelitis (NVO) presents diagnostic and therapeutic challenges requiring adherence to complex clinical guidelines. The emergence of large language models (LLMs) offers new avenues for real-time clinical decision support, yet their utility in managing NVO has not been formally assessed. Methods: This study evaluated four LLMs—Consensus, Gemini, ChatGPT-4o Mini, and ChatGPT-4o—using 13 standardized questions derived from the 2015 IDSA guidelines. Each model generated 13 responses (n = 52), which were independently assessed by three orthopedic surgeons for accuracy (4-point scale) and comprehensiveness (five-point scale). Results: ChatGPT-4o produced the longest responses (428.0 ± 45.4 words), followed by ChatGPT-4o Mini (392.2 ± 97.4), Gemini (358.2 ± 60.5), and Consensus (213.2 ± 68.8). Accuracy ratings showed that ChatGPT-4o and Gemini achieved the highest proportion of “Excellent” responses (54% and 51%, respectively), while Consensus received only 20%. Comprehensiveness scores mirrored this trend, with ChatGPT-4o (3.95 ± 0.79) and Gemini (3.82 ± 0.68) significantly outperforming Consensus (2.87 ± 0.66). Domain-specific analysis revealed that ChatGPT-4o achieved a 100% “Excellent” accuracy rating in therapy-related questions. Statistical analysis confirmed significant inter-model differences (p < 0.001). Conclusions: Advanced LLMs—especially ChatGPT-4o and Gemini—demonstrated high accuracy and depth in interpreting clinical guidelines for NVO. These findings highlight their potential as effective tools in augmenting evidence-based decision-making and improving consistency in clinical care.