Comparing Chinese-language large language models for caregiver questions about developmental dysplasia of the hip.

Background: Large language models (LLMs) are increasingly used for caregiver-facing health information, but their reliability in Chinese-language pediatric orthopaedics remains uncertain. This study evaluated whether responses to developmental dysplasia of the hip (DDH) questions were clinically accurate, aligned with Chinese guidance, and educationally usable. Methods: We compared ChatGPT-4o and DeepSeek-R1 using 53 Chinese-language DDH questions, including 31 caregiver-oriented frequently asked questions and 22 guideline-derived items. Each model generated one response per question using a standardized single-turn, five-sentence prompt. Six blinded pediatric orthopaedic surgeons rated clinical accuracy and guideline concordance. Paired model comparisons, inter-rater reliability, and exploratory formula-based readability were assessed. Results: DeepSeek-R1 had higher clinical accuracy than ChatGPT-4o across 31 caregiver-oriented questions (mean question-level score 4.70 [SD 0.15] vs. 3.69 [0.28]; Conclusions: Under the tested single-query and five-sentence conditions, DeepSeek-R1 achieved higher expert-rated clinical accuracy and Chinese-guideline concordance. The findings describe specific model-platform configurations rather than a permanent model ranking. Professionally reviewed LLM responses may help clinicians reinforce routine DDH education, but individualized diagnostic and treatment guidance, especially for surgery-related questions, should remain clinician-led. Caregivers should use chatbot information only as a supplementary resource.