MiKid-QA: construction and performance evaluation of a paediatric myopia education question-answering model

Background The surging prevalence of paediatric myopia has overwhelmed clinical education services. While general-purpose large language models (LLMs) are increasingly used for medical information, they often lack the domain-specific precision required for specialised care. This study aimed to develop and evaluate Myopia in Kids Question-Answering (MiKid-QA), a paediatric myopia education question-answering model fine-tuned to support standardised patient education. Methods We conducted a cross-sectional evaluation study. MiKid-QA was developed by fine-tuning the Qwen2.5-32B model using low-rank adaptation (LoRA) on curated professional datasets (2015–2025), including textbooks and expert consensus. Performance was assessed against DeepSeek and GPT-4 through a multicentre, single-blind expert evaluation of 25 standardised clinical scenarios. A panel of specialists rated responses across five dimensions (correctness, completeness, readability, helpfulness and safety) using a 5-point Likert scale. Results MiKid-QA demonstrated superior automated performance compared with its base model (Bilingual Evaluation Understudy: 0.1423 vs 0.0967). In expert evaluations, MiKid-QA achieved significantly higher scores for correctness (4.43±0.63) and safety (4.38±0.59) compared with DeepSeek and GPT-4 (all p0.05), MiKid-QA produced more concise responses and lower reading difficulty than GPT-4 (p Conclusion Specialised fine-tuning on curated myopia-related datasets was associated with higher expert-rated correctness and safety in standardised myopia education scenarios. MiKid-QA may serve as an auxiliary educational tool to support patient communication in myopia care, although further external validation and prospective clinical studies are required before clinical deployment.