A 2026 review provides an extensive synthesis of artificial intelligence (AI) applications in critical care nephrology (CCN), focusing on methodologies such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and generative AI models. Multimodal data integration from electronic health records (EHR), physiologic waveforms, imaging, and bedside monitoring forms the foundation for AI model development, enabling predictions of acute kidney injury (AKI), intradialytic hypotension (IDH), nephrotoxin exposure, and management of continuous kidney replacement therapy (CKRT). The review highlights that predictive ML algorithms demonstrate area under the receiver operating characteristic curve (AUROC) values ranging approximately from 0.78 to 0.97 for AKI onset and progression, yet most models lack prospective multicenter validation and real-world workflow integration. Approaches like conformal prediction enhance transparency by providing calibrated risk confidence intervals, and causal inference methods advance interpretability by estimating effects of interventions, moving beyond correlation to support actionable clinical decision-making. Reinforcement learning shows promise in adaptive, personalized fluid and dialysis management through dynamic adjustments of ultrafiltration rates based on patient-specific feedback. [1]
Generative AI and large language models (LLMs) extend AI applications to clinical documentation, reasoning, and patient education, exemplified by ambient AI scribes that reduce clinician documentation burden while introducing challenges such as hallucination—where AI outputs may be plausible but inaccurate. Mitigation strategies include retrieval-augmented generation anchored in validated clinical resources, chain-of-thought prompting to reveal reasoning steps, and domain-specific fine-tuning to improve nephrology-focused accuracy. Real-world implementations, such as AI-driven AKI alerts and CKRT alarm management tools, have shown improvements in process metrics but have yet to demonstrate significant benefits on critical patient-centered outcomes like mortality or dialysis dependence. Barriers to clinical adoption include alert fatigue, data heterogeneity, economic constraints, and limited clinician trust. The authors emphasize the necessity for rigorous external validation, clinician-centered interface design to minimize cognitive load, adaptive regulatory frameworks, and ongoing postdeployment monitoring. The review concludes that while AI holds transformative potential across risk prediction, precision nephrology, and workflow augmentation, translating technical advances into measurable clinical improvements requires multidisciplinary collaboration, ethical oversight, and sustainable integration strategies. [1]
A 2022 review examined the application of ML techniques for predicting AKI within intensive care units (ICUs). The review delineates several ML methodologies, including supervised, unsupervised, semi-supervised, and reinforcement learning, emphasizing their unique roles in AKI prediction, prognostication, and management. Supervised algorithms—such as Random Forest, Gradient Boosting Machines, Extreme Gradient Boosting (XGBoost), artificial neural networks (ANN), and recurrent neural networks like long short-term memory (LSTM)—have been trained on large critical care datasets such as Medical Information Mart for Intensive Care (MIMIC-III) and eICU to predict AKI onset up to 48 hours in advance, with reported AUROC values frequently exceeding 0.80. For instance, convolutional neural networks utilizing urine output data achieved an AUROC of 0.89 for predicting stage II and III AKI, while ensemble time-series models incorporating both explicit indicators (vital signs, labs) and implicit indicators (medication administration) improved predictive performance when medication data were included. Models predicting AKI-related adverse outcomes such as mortality and need for renal replacement therapy (RRT) leveraged algorithms like support vector machines (SVM) and random forests, demonstrating AUROCs up to 0.87 and identifying clinical variables such as urine output, systolic blood pressure, age, serum bicarbonate, and heart rate as key prognostic factors. [2]
The review also highlights efforts to refine AKI management using ML, for example, predicting fluid responsiveness in oliguric patients, where an XGBoost model achieved an AUROC of 0.86 based on features including urinary creatinine, blood urea nitrogen, albumin, and maximum temperature. Furthermore, models predicting hypotensive episodes post-continuous renal replacement therapy (CRRT) initiation demonstrated comparable performance using XGBoost and deep neural networks (AUROCs of 0.83 and 0.82, respectively), informing potential adjustments in CRRT prescription. Specialized populations such as pediatric ICU patients and surgical ICU cohorts have been the focus of tailored predictive models, achieving AUROCs around 0.80 and incorporating interpretable features to avoid "black box" limitations. Beyond prediction, ML analyses of biochemical data have underscored subphenotypes of AKI with distinct clinical outcomes and metabolomic profiles, such as reduced urinary glycine and ethanolamine levels associated with AKI. Although the review primarily focuses on predictive model development and performance, it also discusses potential ML-guided interventions, including alerting clinicians to avoid or discontinue nephrotoxic medications in high-risk patients. However, it provides limited discussion of the implementation of such approaches in clinical practice. [2]