Applications of artificial intelligence in chronic kidney disease: a systematic review.

OBJECTIVE

To identify the applications of artificial intelligence in the early diagnosis, prediction and management of chronic kidney disease, highlighting the algorithms used, clinical outcomes, and impacts on nephrology practice.

METHODS

In this systematic review, PubMed and Scopus databases were searched without year or language restrictions, including studies published between 2019 and 2025. Article selection followed PRISMA 2020 criteria, considering patients at risk for or diagnosed with chronic kidney disease, artificial intelligence tools as interventions, and outcomes related to early detection and disease management. Data was extracted on algorithm type, sample size, clinical outcomes, and main findings.

RESULTS

Twenty-six studies were included, demonstrating the predominance of supervised learning and deep learning algorithms, generally showing high-performance metrics such as accuracy, sensitivity, and specificity in identifying chronic kidney disease and its stages. However, recurring weaknesses were observed, especially regarding the control of confounding factors, external validation of models, and standardization of datasets. Critical analysis of the studies shows that, despite the promising potential of computational models in the management of chronic kidney disease, methodological gaps still exist that limit their generalization and incorporation into clinical practice.

CONCLUSION

This review demonstrates that artificial intelligence is a strategic tool for prevention, early diagnosis, and individualized management of chronic kidney disease. However, progress in this area depends on the development of more robust models, externally validated and integrated into real clinical contexts, contributing safely and effectively to renal health care.