Evaluating large language models as tools for public health education on scoliosis

Abstract

Purpose This study was aimed to compare the efficacy of three most popular large language models (LLMs)—Claude Opus 4.6, ChatGPT Thinking 5.4 and DeepSeek v3.2 in answering frequently asked questions (FAQs) about scoliosis. Methods 20 scoliosis related questions (four categories, five questions in each category) were submitted to each LLM. A panel of 9 experts (two spine surgeons, two pediatric orthopedic surgeons and five physical therapists, all blinded to the LLMs and responses) rated independently each response generated by LLMs on a 6 points Likert scale (1 as strongly disagree to 6 as strongly agree). 540 total ratings were collected. Intergroup comparisons were conducted by Kruskal Wallis test and Mann Whitney U pairwise tests. Paired question level analysis was achieved by Friedman test and Wilcoxon signed rank comparisons. Results Claude’s score was 5.53 ± 0.76 much higher than both ChatGPT (4.84 ± 0.86, p < 0.001) and DeepSeek (4.86 ± 0.84, p < 0.001), but no difference was found between ChatGPT and DeepSeek (p = 0.749). Claude performed on top for 19 of 20 questions (95%) and was favored by 7 of 9 reviewers. Consistency of Claude was also highest [CV = 13.8% vs. 17.9% (ChatGPT) and 17.2% (DeepSeek)]. Conclusion Although all three LLMs achieved favorable overall ratings (>4.8/6), Claude performed significantly better than ChatGPT and DeepSeek for scoliosis FAQs taking into account its higher accuracy and consistency. Within the scope of the present evaluation, Claude demonstrated the strongest overall performance among the three LLMs tested.