Personalized Prediction in Nephrology: A Comprehensive Review of Artificial Intelligence Models Using Biomarker Data

Background/Objectives: This review paper summarizes and critically analyzes different Machine Learning (ML) and Artificial Intelligence (AI)-based predictive modeling techniques in early detection and personalized treatment for Kidney diseases, specifically diabetic kidney disease (DKD), chronic kidney disease (CKD), and end-stage renal disease (ESRD). This manuscript focuses on integrating electronic medical record (EMR) data with multi-omics biomarkers to enhance clinical decision-making. Method: A systematic database search retrieved 43 peer-reviewed articles from PubMed, Google Scholar, and ScienceDirect. These works were critically analyzed based on methodological rigor, model interpretability, and translational potential. Review: This paper examined a series of advanced AI and ML models, including but not limited to Random Forests (RF), Extreme Gradient Boosting (XGBoost), deep neural networks, and artificial neural networks, among others. Additionally, this paper explicitly explored validated approaches for fibrosis staging, dialysis prediction, and mortality risk assessment. Conclusions: The paper shows how leveraging AI models for patient-specific biomarker and EMR data presents substantial promise for facilitating preventative interventions, guiding timely nephrology referrals, and optimizing individualized treatment regimens. These state-of-the-art tools will ultimately improve long-term renal outcomes and reduce healthcare burdens. The study further addresses ethical challenges and potential adverse implications associated with the use of AI in clinical settings.