Abstract
Artificial intelligence (AI) and machine learning (ML) are transforming nephrology by enhancing diagnosis, risk prediction, and treatment optimization for conditions such as acute kidney injury (AKI) and chronic kidney disease (CKD). AI-driven models utilize diverse datasets-including electronic health records, imaging, and biomarkers-to improve clinical decision-making. Applications such as convolutional neural networks for kidney biopsy interpretation, and predictive modeling for renal replacement therapies underscore AI's potential. Nonetheless, challenges including data quality, limited external validation, algorithmic bias, and poor interpretability constrain the clinical reliability of AI/ML models. To address these issues, this article offers a structured framework for nephrologist peer reviewers, integrating the TRIPOD-AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis-AI Extension) checklist. Key evaluation criteria include dataset integrity, feature selection, model validation, reporting transparency, ethics, and real-world applicability. This framework promotes rigorous peer review and enhances the reproducibility, clinical relevance, and fairness of AI research in nephrology. Moreover, AI/ML studies must confront biases-data, selection, and algorithmic-that adversely affect model performance. Mitigation strategies such as data diversification, multi-center validation, and fairness-aware algorithms are essential. Overfitting in AI is driven by small patient cohorts faced with thousands of candidate features; our framework spotlights this imbalance and offers concrete remedies. Future directions in AI-driven nephrology include multimodal data fusion for improved predictive modeling, deep learning for automated imaging analysis, wearable-based monitoring, and clinical decision support systems (CDSS) that integrate comprehensive patient data. A visual summary of key manuscript sections is included.