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To assess the efficacy of AI models in predicting global chronic kidney disease incidence and their implications for public health.

The Power of Multiple Artificial Intelligence Models to Predict Global Chronic Kidney Disease Incidence: Who Leads the Race?

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To assess the efficacy of AI models in predicting global chronic kidney disease incidence and their implications for public health.
Collected CKD incidence data from 21 Global Burden of Disease regions from 1990 to 2021.
Used five AI models and two traditional forecasting methods for predicting CKD incidence for 2023.
Compared predicted values against actual observed data to evaluate model performance.
AI models and traditional models showed similar performance with high accuracy in various regions.
GPT-4o had the highest mean accuracy of 0.722, with all models exceeding 0.65 accuracy.
No significant accuracy difference was found between AI and traditional models (ANOVA p = 0.27).

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Machine learning predicts chronic kidney disease incidence in 21 regions, suggesting improvements for public health policy.

Qing et al. (2025) studied this question.

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