A Multidimensional Comparative Study of Multiple Mainstream Large Language Models in Perioperative Consultation for Hypospadias Surgery (Preprint)

BACKGROUND Hypospadias is a common congenital malformation requiring surgical correction, with caregivers facing significant perioperative information needs. Large language models (LLMs) offer a potential solution for health education, yet their performance in pediatric urological consultations remains unexplored. OBJECTIVE This study aimed to evaluate the performance of five LLMs—ChatGPT-4o, Gemini-2.5-Pro, OpenEvidence, Zhipu Qingyan, and DeepSeek—in addressing core concerns of caregivers during the perioperative period for children with hypospadias, and to assess their application value and limitations in pediatric urological clinical health education. METHODS A prospective, non-interventional, cross-sectional design was employed. A question bank was developed based on literature and clinical practice, with 10 high-priority questions selected via questionnaire screening for the test set. Seven experts (6 dimensions) and 32 caregivers (4 dimensions) were prospectively recruited to evaluate responses using a double-blind forced-order ranking method (reverse scoring from 1 to 5), with reference authenticity being verified. RESULTS Significant differences existed across dimensions among the five models (P