Four general‐purpose large language models (ChatGPT‐5, Claude 4, Grok 4 and Gemini 2.5) show comparable performance in specialised total knee arthroplasty clinical questions

PURPOSE: To evaluate and compare the performance of four general-purpose large language models (LLMs) (ChatGPT-5, Claude 4, Grok 4 and Gemini 2.5) in answering specialised clinical questions related to total knee arthroplasty (TKA) derived from the World Expert Meeting in Arthroplasty (WEMA). METHODS: This is a cross-sectional comparative study. Twenty questions on TKA supported by moderate-strong level of evidence were randomly selected from the WEMA. Three orthopaedic surgeons independently performed a blinded assessment of all LLM-generated responses. An adapted version of the QUEST rating system, a comprehensive framework designed for the objective human assessment of LLM performance across healthcare-related subdomains, was used. Furthermore, the same three evaluators subjectively selected the best-performing LLM response for each question. RESULTS: The four LLMs presented statistically significant differences in overall performance based on the QUEST framework (score range 1-5): Gemini 2.5; 4.77 ± 0.06, Claude 4; 4.72 ± 0.07, ChatGPT-5; 4.70 ± 0.08 and GROK 4; 4.62 ± 0.09 (p < 0.001). Gemini 2.5 achieved the highest scores in the Accuracy (4.58 ± 0.70), Comprehensiveness (4.87 ± 0.34) and Trust (4.52 ± 0.62) dimensions. However, Claude 4 obtained the highest score for the Currency (4.23 ± 0.67) dimension. When assessors subjectively selected the superior answer for each question, Claude 4 was chosen most frequently, in 46.7% of cases, followed by ChatGPT-5 in 25.4%, Gemini 2.5 in 22.9% and GROK 4 in 7.5% of cases. CONCLUSIONS: Four general-purpose LLMs (ChatGPT-5, Claude 4, Grok 4 and Gemini 2.5) demonstrated good overall performance when addressing specialised clinical questions related to TKA. No single model consistently outperformed the others across all evaluated domains. LEVEL OF EVIDENCE: Level V.