Artificial Intelligence and Machine Learning for Prognostication Across the AKI–AKD–CKD Continuum: Predictive Models, Clinical Outcomes, and the Path Toward Clinical Translation

Introduction Acute kidney injury affects about one in five hospitalised adults and substantially increases the risk of chronic kidney disease, yet conventional scoring systems poorly identify patients who will progress. This review critically appraises artificial intelligence and machine learning models for prognostication across the acute kidney injury–acute kidney disease–chronic kidney disease continuum. Methods A narrative review of 48 source articles was undertaken, covering persistent kidney injury, renal recovery, dialysis dependence, major adverse kidney events, mortality, and chronic kidney disease. Studies were examined for prediction endpoint, injury aetiology, algorithm, dataset, validation strategy, interpretability, and clinical applicability. Results Reported discrimination varied substantially across algorithms, endpoints, populations, datasets, and validation strategies, with several models outperforming conventional comparators in within-study comparisons. Serum creatinine dynamics, blood urea nitrogen, age, vasopressor use, urine output, and measures of comorbidity, including the Charlson Comorbidity Index (CCI), were recurrent predictors, although their importance varied by population and endpoint. Fewer than one-third of studies were externally validated; most were retrospective and at high risk of bias, calibration and fairness were seldom assessed, and no randomised evidence of improved patient outcomes exists. Discussion The evidence comprises heterogeneous, non-equivalent prediction tasks rather than a single modelled transition to chronic kidney disease. Cross-study performance comparisons are therefore unreliable, and post-hoc explanation tools cannot replace inherent interpretability, calibration, and prospective validation. Conclusion Artificial intelligence shows promise for prognostication after acute kidney injury, but clinical translation requires clearly defined endpoints, aetiology-aware modelling, prospective multicentre validation, fairness assessment, and randomised trials demonstrating patient benefit.