Exploring and Comparing the Use of Large Language Models in Supporting Osteoporosis Health Consultations.

Purpose: To compare the medical accuracy and content comprehensiveness of three large language models (LLMs) in generating responses to frequently asked osteoporosis-related questions and to determine their potential role in clinical support. Methods: Twenty-five questions covering six clinical domains were submitted to each model in isolated sessions. Five senior orthopedic physicians, each with over 25 years of clinical experience, independently rated the medical accuracy of each response using a 5-point Likert scale. Responses rated as "acceptable" or above were further evaluated for content comprehensiveness. Statistical analysis included the Kruskal-Wallis test and Dunn's post hoc test with Bonferroni correction. Results: A total of 75 unique responses (25 questions × 3 models) were evaluated by five orthopedic experts, yielding 375 ratings. ChatGPT-4o achieved the highest accuracy score (median: 4.6; IQR: 4.4-4.8), significantly outperforming Gemini-2.5 Pro (p=0.039) and DeepSeek-R1 (p<0.001). For content comprehensiveness, both ChatGPT-4o and Gemini-2.5 Pro had a median score of 4.4, higher than DeepSeek-R1 (median: 4.2), though differences did not reach statistical significance (p=0.0536). Gemini-2.5 Pro was noted for its fluent and user-friendly language but lacked clinical depth in some responses. DeepSeek-R1, despite offering source citations, demonstrated greater inconsistency. Conclusion: LLMs have clear potential as tools for patient education in osteoporosis. ChatGPT-4o demonstrated the most balanced and clinically reliable performance. Nonetheless, expert medical oversight remains essential to ensure safe and context-appropriate use in healthcare settings.