Artificial intelligence prediction algorithms for refractive error onset and progression in children and adolescents: A systematic review and meta-analysis.

Eligible studies involved children and adolescents (≤18 years) and applied artificial intelligence-based technologies to predict the onset or progression of refractive error, using non-image-based input data.

Between-study heterogeneity was substantial (τ 2 = 0.0695, I 2 = 100%), indicating major differences across models.

Across all iterations, RMSE estimates ranged narrowly from 0.48 to 0.52 D, with confidence intervals overlapping the main analysis.

The studies' quality assessment showed mostly low concern, apart from the analysis domain, in which 43.8% of studies were rated high risk.