{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:52:46Z","timestamp":1791309166987,"version":"4.3.1"},"reference-count":52,"publisher":"BMJ","funder":[{"name":"Haihe Lab of ITAI Science and Technology Project","award":["25HHXCSS00007"],"award-info":[{"award-number":["25HHXCSS00007"]}]},{"name":"Tianjin Key Medical Discipline Construction Project","award":["TJYXZDXK-3-004A-2"],"award-info":[{"award-number":["TJYXZDXK-3-004A-2"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Br J Ophthalmol"],"accepted":{"date-parts":[[2026,9,21]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u20132025), 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u00b10.63) and safety (4.38\u00b10.59) compared with DeepSeek and GPT-4 (all p&lt;0.001). While completeness and helpfulness were comparable (p&gt;0.05), MiKid-QA produced more concise responses and lower reading difficulty than GPT-4 (p&lt;0.001), suggesting a potentially favourable balance between information accuracy and patient accessibility in standardised educational scenarios.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2026-329816","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:20:19Z","timestamp":1791307219000},"page":"bjo-2026-329816","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["MiKid-QA: construction and performance evaluation of a paediatric myopia education question-answering model"],"prefix":"10.1136","author":[{"given":"Zixun","family":"Wang","sequence":"first","affiliation":[{"name":"Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, 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