Artificial Intelligence in Acute Kidney Injury Prediction: Challenges and Opportunities in Low-Income Settings

Background Acute kidney injury (AKI) remains a major cause of morbidity and mortality worldwide1. Most cases occur in low- and middle-income countries (LMICs), where delayed diagnosis and limited access to nephrology care worsen outcomes2. Artificial intelligence (AI) tools can predict AKI hours before serum creatinine rises, enabling earlier intervention3,4. However, most existing models are developed and validated in high-income settings, relying on continuous electronic data infrastructures that are rarely available in LMICs, raising concerns about generalizability and equity. Methods A narrative review of studies published between 2015 and 2025 identified AI-based AKI prediction models. Articles were assessed for study design, input variables, validation strategies, performance metrics, and applicability to low-resource environments. Contextual clinical insights from North African nephrology practice, including common AKI etiologies such as sepsis and obstetric complications, were incorporated to enhance relevance. Results Most AI models rely on large datasets, time-series analyses, and complex algorithms, achieving high predictive performance3–5. However, external validation in LMIC populations is lacking, and model performance may be affected by regional differences in AKI etiology and data availability. Simplified, interpretable models using routinely available clinical variables such as serum creatinine, urine output, blood pressure, age, and sepsis status may still provide clinically meaningful early warning signals in low-resource settings. Conclusion AI offers a promising opportunity for earlier AKI detection worldwide. To ensure equitable clinical benefit, models must be contextually adapted, ethically validated, and integrated into routine care through simplified, open-access approaches supported by regional collaborations led by nephrologists in LMICs.