Supplementary Material for: The Power of Multiple AI Models to Predict Global CKD Incidence: Who Leads the Race?

Background:

The global incidence of chronic kidney disease (CKD) continues to rise, but delayed epidemiological data pose challenges to public health policy. Traditional surveillance methods often suffer from reporting delays. Recent advances in artificial intelligence (AI) offer novel opportunities for enhancing disease burden predictions.

Methods:

We collected CKD incidence data from 21 Global Burden of Disease (GBD) regions spanning from 1990 to 2021. Using five advanced AI models (GPT-4, Claude-3.7, DeepSeek-r1, Grok-3, and Gemini-2.5) and two traditional forecasting methods (Autoregressive Integrated Moving Average and Bayesian Age-Period-Cohort), we predicted CKD incidence for 2023. The performance of the models was evaluated by comparing the predicted values to the actual observed data. All models were trained using the same data and instructions.

Results:

The AI models and traditional models performed similarly, with near-perfect accuracy in predicting incidence rates in regions such as the Americas, Central Europe, East Asia, High-income Asia Pacific, Southeast Asia, and Tropical Latin America. Among the models, GPT-4o demonstrated the highest mean accuracy of 0.722, with all models achieving average accuracies above 0.65. No statistically significant difference in accuracy was observed between AI-based and traditional models (ANOVA p=0.27).

Conclusion:

State-of-the-art AI models, when systematically prompted and standardized, can predict global CKD incidence with accuracy comparable to traditional statistical models. AI-driven epidemiological forecasting holds promise for enhancing real-time public health planning and resource allocation, particularly in regions with stable historical data.