Prediction of refractive error in adolescents using a multimodal large language model.

Background: This study aims to develop and evaluate a multimodal large language model (LLM) for predicting refractive error (diopter) in adolescents by integrating fundus images with clinical and demographic data. The goal is to demonstrate how such a model can serve as a digital health tool for early screening and personalized management of refractive conditions in clinical and remote health settings. Methods: A dataset of 16,226 annotated records from adolescents aged 2 to 18 years was used. A vision-language foundation model based on Qwen2.5-VL was fine-tuned using supervised learning, incorporating both imaging and clinical data to predict spherical equivalent (SE). The model was trained on a randomly split dataset, and performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), coefficient of determination ( Results: The model achieved a mean absolute error of 0.647 diopters and showed strong predictive performance across most refractive subgroups. The incorporation of multimodal data significantly outperformed single-modality models. Visualization analyses confirmed that the model's predictions are clinically interpretable and reliable. Conclusions: This multimodal LLM demonstrates the potential of digital health technologies to enhance refractive error prediction in adolescents. Its integration into digital health platforms can provide a non-invasive, scalable solution for early myopia screening, making it an invaluable tool for clinicians in both remote and urban settings.