The Power of Multiple Artificial Intelligence Models to Predict Global Chronic Kidney Disease Incidence: Who Leads the Race?
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.
Authors
- Wisit Cheungpasitporn (ORCID: https://orcid.org/0000-0001-9954-9711)
- Jianbo Qing (ORCID: https://orcid.org/0000-0002-6146-3389)
- Xiao Wang (ORCID: https://orcid.org/0000-0002-6203-6645)
- Yafeng Li (ORCID: https://orcid.org/0000-0002-7500-0959)
- Junnan Wu (ORCID: https://orcid.org/0009-0001-5621-4011)
- Kaili Qin (ORCID: https://orcid.org/0000-0001-9575-9279)
Publication Details
- Journal
- American Journal of Nephrology
- Published
- 2025-12-12
- DOI
- https://doi.org/10.1159/000549005
- Primary Topic
- Chronic Kidney Disease and Diabetes
- Type
- article
- Field-Weighted Citation Impact
- 0.00