From chronic kidney disease classification to genuine early prediction: a systematic review of artificial intelligence, methodological quality, and translational evidence
6 affiliations
- 1Department of Cyber Security, Federal University Dutsin-Ma , ,
- 2Department of Information Technology, Bayero University , ,
- 3Department of Computer Science, Bayero University , ,
- 4School of Digital Engineering and Artificial Intelligence, Euromed University of Fes , ,
- 5School of Engineering and Computer Science, Victoria University of Wellington , ,
- 6Department of Computer Science, Blekinge Institute of Technology , ,
Abstract
Introduction: Artificial intelligence has been widely applied to chronic kidney disease (CKD), but the term ‘prediction” is frequently used for tasks ranging from contemporaneous disease classification to genuine forecasting of future renal outcomes. This systematic review examined this distinction while evaluating methodological quality and evidence for clinical translation.
Methods: A predefined Scopus-based search covering 2021–2026 identified eligible AI- and machine-learning-based CKD studies. After screening and duplicate reconciliation, 169 unique primary studies were included. Future-oriented prediction required an identifiable baseline, predictors available at or before baseline, a subsequent renal outcome, and a meaningful prediction horizon. Studies were evaluated for modeling approaches, datasets, validation, calibration, explainability, clinical usefulness, and methodological quality using PROBAST+AI.
Results: Of 169 studies, 122 (72.2%) addressed current-state CKD detection or classification, while only 46 (27.2%) contained a clearly future-oriented prediction component. Seventeen studies used geographic or institutional external validation and only four used an independent external cohort or dataset. Calibration was assessed in 12 studies and explicit explainability methods were reported in 16. PROBAST+AI identified high overall risk of bias in 156 studies during development and 164 during evaluation, with no study achieving an overall low-risk judgement.
Discussion: CKD-AI evidence is substantially stronger for retrospective classification than for genuine early prediction or clinical translation. Future research should prioritize longitudinal prediction, independent and temporal validation, leakage-resistant analysis, calibration, clinically validated explainability, and prospective real-world evaluation.
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BibTeX
@article{suleiman2026from,
title = {{From chronic kidney disease classification to genuine early prediction: a systematic review of artificial intelligence, methodological quality, and translational evidence}},
author = {Aminu Bashir Suleiman and Ahmad Tijjani Garba and Aisha Mustapha Ahmad and Kaloma Usman Majikumna and Harisu Abdullahi Shehu and Huseyin Kusetogullari},
journal = {Frontiers in artificial intelligence},
year = {2026},
month = oct,
doi = {10.3389/frai.2026.1976937},
url = {https://europepmc.org/article/PMC/PMC13724554},
note = {CC BY},
}
RIS
TY - JOUR
TI - From chronic kidney disease classification to genuine early prediction: a systematic review of artificial intelligence, methodological quality, and translational evidence
AU - Aminu Bashir Suleiman
AU - Ahmad Tijjani Garba
AU - Aisha Mustapha Ahmad
AU - Kaloma Usman Majikumna
AU - Harisu Abdullahi Shehu
AU - Huseyin Kusetogullari
PY - 2026
DA - 2026/10/19
JO - Frontiers in artificial intelligence
DO - 10.3389/frai.2026.1976937
UR - https://europepmc.org/article/PMC/PMC13724554
AB - Introduction: Artificial intelligence has been widely applied to chronic kidney disease (CKD), but the term ‘prediction” is frequently used for tasks ranging from contemporaneous disease classification to genuine forecasting of future renal outcomes. This systematic review examined this distinction while evaluating methodological quality and evidence for clinical translation. Methods: A predefined Scopus-based search covering 2021–2026 identified eligible AI- and machine-learning-based CKD studies. After screening and duplicate reconciliation, 169 unique primary studies were included. Future-oriented prediction required an identifiable baseline, predictors available at or before baseline, a subsequent renal outcome, and a meaningful prediction horizon. Studies were evaluated for modeling approaches, datasets, validation, calibration, explainability, clinical usefulness, and methodological quality using PROBAST+AI. Results: Of 169 studies, 122 (72.2%) addressed current-state CKD detection or classification, while only 46 (27.2%) contained a clearly future-oriented prediction component. Seventeen studies used geographic or institutional external validation and only four used an independent external cohort or dataset. Calibration was assessed in 12 studies and explicit explainability methods were reported in 16. PROBAST+AI identified high overall risk of bias in 156 studies during development and 164 during evaluation, with no study achieving an overall low-risk judgement. Discussion: CKD-AI evidence is substantially stronger for retrospective classification than for genuine early prediction or clinical translation. Future research should prioritize longitudinal prediction, independent and temporal validation, leakage-resistant analysis, calibration, clinically validated explainability, and prospective real-world evaluation.
SN - 2624-8212
ER -