ArticleOphthalmology and therapy2025
Evaluating the Efficacy of Large Language Models in Guiding Treatment Decisions for Pediatric Refractive Error.
Article in Ophthalmology and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluation of large language models and retrieval-augmented generation for clinical reasoning in pediatric myopia: a 50-case real-world study.Scientific reports · 2026Article
- Large Language Models Using Clinical Text in Pediatrics: A Scoping Review.JAMA network open · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Assessing the Quality of AI Responses to Patient Concerns About Axial Spondyloarthritis: Delphi-Based Evaluation.JMIR AI · 2026Article
- Benchmarking publicly accessible large language models for high-myopia multiple-choice question generation in digital ophthalmic education and public health training.Frontiers in public health · 2026Article
- Assessing large language models as assistive tools in selecting first trial lens parameters for orthokeratology.Frontiers in medicine · 2026Article
- Evaluating the Applicability of Advanced Large Language Models in Laboratory Medicine Test Questions: A Comparative Performance Study.Advances in medical education and practice · 2026Article
- Prediction of refractive error in adolescents using a multimodal large language model.Frontiers in medicine · 2026Article
- ChatGPT-5 versus other mainstream large language models in core diabetic retinopathy patient queries.Frontiers in cell and developmental biology · 2026Article
- Digital technologies in enhancing hierarchical vision health management for the next 1000 days of children's life: multi-component economic evaluation.The Lancet regional health. Western Pacific · 2025Article
- Comparative performance of large language models for patient-initiated ophthalmology consultations.Frontiers in public health · 2025Article
- Large language models in the management of chronic ocular diseases: a scoping review.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
introductionEffective management of pediatric myopia, which includes treatments like corrective lenses and low-dose atropine, requires accurate clinical decisions. However, the complexity of pediatric refractive data, such as variations in visual acuity, axial length, and patient-specific factors, pose challenges to determining optimal treatment. This study aims to evaluate the performance of three large language models in analyzing these refractive data.
methodsA dataset of 100 pediatric refractive records, including parameters like visual acuity and axial length, was analyzed using ChatGPT-3.5, ChatGPT-4o, and Wenxin Yiyan, respectively. Each model was tasked with determining whether intervention was needed and subsequently recommending a treatment (eyeglasses, orthokeratology lens, or low-dose atropine). The recommendations were compared to professional optometrists' consensus, rated on a 1-5 Global Quality Score (GQS) scale, and evaluated for clinical safety utilizing a three-tier accuracy assessment.
resultsChatGPT-4o outperformed both ChatGPT-3.5 and Wenxin Yiyan in determining intervention needs, with an accuracy of 90%, significantly higher than Wenxin Yiyan (p < 0.05). It also achieved the highest GQS of 4.4 ± 0.55, surpassing the other models (p < 0.001), with 85% of responses rated as "good" ahead of ChatGPT-3.5 (82%) and Wenxin Yiyan (74%). ChatGPT-4o made only eight errors in recommending interventions, fewer than ChatGPT-3.5 (12) and Wenxin Yiyan (15). Additionally, it performed better with incomplete or abnormal data, maintaining higher quality scores.
conclusionChatGPT-4o showed better accuracy and clinical safety, making it a promising tool for decision support in pediatric ophthalmology, although expert oversight is still necessary.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.