Artificial Intelligence in Critical Care Nephrology

Abstract

Artificial intelligence (AI), including machine learning, deep learning, reinforcement learning, and generative AI, has the potential to advance critical care nephrology (CCN) by enhancing prediction accuracy, improving diagnostic capabilities, supporting clinical decision making, and streamlining workflow processes. Current applications in CCN include AKI prediction, nephrotoxin surveillance, intradialytic hypotension forecasting, and AI-guided dialysis and continuous KRT management, with performance often exceeding traditional models. However, the effect on patient-centered outcomes such as mortality, dialysis dependence, and cost-effectiveness remains uncertain. Emerging techniques, such as conformal prediction for calibrated risk estimates, causal inference for intervention modeling, and reinforcement learning for adaptive ultrafiltration, show promise in enhancing reliability, interpretability, and individualized care. Generative AI and large language models extend these applications to clinical documentation, reasoning, and patient education, while raising new challenges, including hallucinations, regulatory oversight, and clinician trust. Persistent barriers such as data heterogeneity, limited external validation, alert fatigue, and economic constraints hinder broad adoption. This review synthesizes the current evidence and outlines four priorities for advancing AI in CCN: (1) rigorous multicenter validation focused on clinical outcomes, (2) integration of uncertainty quantification and causal modeling into AI tools, (3) development of clinician-centered interfaces that minimize cognitive load, and (4) establishment of transparent, adaptive regulatory and governance frameworks. Realizing the promise of AI in CCN will require multidisciplinary collaboration, fairness and generalizability testing, and sustainable implementation strategies that align technologic innovation with measurable improvements in patient care.

Introduction

Artificial intelligence (AI) has the potential to transform critical care nephrology (CCN),^1–3 a subspecialty characterized by complex physiology, high-dimensional data, and time-sensitive decision making. CCN addresses AKI, severe fluid/electrolyte disorders, and advanced CKD complications in multiorgan failure, making it well suited for AI innovation.^1,4–6

Multimodal intensive care unit (ICU) data have driven advanced AI models. Unlike traditional analytics, AI methods, including machine learning (ML), deep learning, reinforcement learning (RL), model predictive control, and generative AI, enable dynamic modeling of trajectories, responses, and resource use (Table 1).^1,7

Table 1.

Artificial intelligence approaches in critical care nephrology: an overview

Methodology	Definition	Potential Benefits	Limitations
ML	Algorithms that identify patterns in structured and unstructured data to make predictions or classifications without explicit programming rules	Handles large, heterogeneous datasets; improves predictive accuracy over traditional models; and adaptable to evolving data	Performance depends on data quality and representativeness; risk of overfitting; and limited interpretability in some models
DL	A subset of ML using multilayered neural networks to learn complex, hierarchical data representations, particularly effective for high-dimensional inputs such as images or waveforms	Excels at image, audio, and signal analysis; enables automated feature extraction; supports multimodal integration	Requires large annotated datasets; computationally intensive; often lacks transparency (black box)
RL	Sequential decision-making framework where an agent learns optimal actions via trial-and-error interactions with an environment, guided by reward signals	Enables adaptive, personalized treatment strategies; effective for optimizing processes over time	Slow convergence; high sensitivity to environment design; ethical concerns in patient-facing applications
MPC	A control algorithm that uses a dynamic model of the system to predict future states and optimize control actions within defined constraints	Effective for real-time physiologic regulation (e.g., ventilation and fluid management); incorporates safety limits	Requires accurate system models; computationally demanding; performance degrades if model assumptions are violated
Generative AI	Models that learn underlying data distributions to generate new, realistic outputs (e.g., text, images, and simulations) conditioned on input prompts	Can synthesize training scenarios, augment datasets, and support clinical documentation; facilitates hypothesis generation	Risk of generating inaccurate or biased outputs; requires careful validation before clinical use

AI, artificial intelligence; DL, deep learning; ML, machine learning; MPC, model predictive control; RL, reinforcement learning.

This review highlights AI in CCN for AKI, dialysis, and stratification, outlining gaps, barriers, and priorities.

AI and Data Growth in CCN

In CCN, the explosion of high-frequency data, ranging from continuous KRT (CKRT) waveforms to real-time electronic health record (EHR) feeds, has shifted AI from static prediction to dynamic, multimodal decision support.¹ Multimodal models show promise for fluid assessment and AKI detection, but data artifacts, missing information, and interoperability challenges remain, limiting reliability and broader clinical application. Deployable tools, therefore, require harmonized data.^8,9

AI in CCN relies on multimodal data, including EHRs, waveforms, imaging, and device logs, which require preprocessing and harmonization before modeling.^1,8 Figure 1 illustrates how this pipeline produces predictive, RL, and generative models supporting clinical and operational insights. Continuous retraining with patient feedback underscores the need for learning systems. Success requires harmonization using standards and federated learning, with transparent reporting and governance to ensure reproducibility and privacy.¹⁰

Figure 1.

AI-driven data ecosystem in CCN. This schematic illustrates the integration of multimodal data—including EHRs, physiologic waveforms, imaging, and bedside inputs—into preprocessing pipelines for AI model development. It delineates the feedback loop, enabling continuous model retraining and adaptation. Distinct AI methodologies—predictive, RL, and generative models—are shown to translate data into clinical decision support and operational insights. AI, artificial intelligence; CCN, critical care nephrology; CKRT, continuous KRT; CT, computed tomography; EHR, electronic health record; IDH, intradialytic hypotension; MRI, magnetic resonance imaging; RL, reinforcement learning; SpO₂, peripheral capillary oxygen saturation; UF, ultrafiltration.

AI for AKI: Prediction, Prevention, and Intervention

AKI affects 10%–15% of hospitalized patients and over 50% of those in intensive care, frequently leading to CKD, dialysis dependence, or death.^4,9 Traditional biomarkers, such as serum creatinine, are delayed and nonspecific, often resulting in missed opportunities for early intervention. AI has emerged as a promising tool to enhance earlier detection, stratify risk, and guide both preventive and therapeutic strategies across the AKI continuum.^1,5,11–17

Prediction and Early Detection

Multimodal AI models that integrate structured EHR data, including vital signs, laboratory values, and medication exposures, with unstructured inputs such as waveform signals and clinical documentation, can predict AKI onset hours to days in advance.² For example, the widely used Epic Risk of Hospital-Acquired AKI model demonstrated moderate discrimination (area under the receiver operating characteristic curve [AUROC], approximately 0.77) with a lead time of 21.6 hours in a 40,000-patient cohort. However, it exhibited poor calibration and a low positive predictive value, raising alarm fatigue concerns.¹⁸

Koyner et al.² reported AUROCs of 0.88–0.93 in 420,000 patients, but low AKI incidence limited precision-recall (area under the precision-recall curve, approximately 0.20), underscoring the gap between AUROC and utility.

Meta-Analytic Evidence

A systematic review of 95 studies, including more than 3.8 million hospital admissions, identified 302 unique ML models for AKI prediction.³ Pooled AUROCs ranged from 0.78 to 0.87; however, only a minority of models underwent external validation, and most of the studies were assessed as having a high risk of bias.³ Heterogeneity in definitions and modeling limits generalizability.

AKI is better viewed as a continuum,¹⁹ from early injury to persistent severe forms, each with distinct prognostic and therapeutic implications. An external validation model for persistent severe AKI achieved an AUROC of 0.97,¹⁹ but most lack prospective validation, clinician-in-loop testing, or workflow integration needed for real-world use.³ Moreover, current AI tools typically function as passive risk stratification rather than actionable decision support systems, failing to bridge the gap between prediction and intervention.¹ Future efforts should emphasize rigorous external validation, context-specific deployment, and integration with care pathways to enable timely, stage-appropriate interventions to prevent AKI progression.

AI-Guided Interventions: Alerts and Decision Support

Clinical decision support systems represent one of the most advanced applications of AI in CCN. Trials such as ELAIA-2 and KAT-AKI evaluated AI-driven alerts and multidisciplinary recommendations for AKI management.^20,21 They modestly improved processes but not mortality or dialysis. These models improve processes, but demonstrating the benefits of outcomes requires larger trials.^20,21

Excessive alerts cause fatigue and reduce responsiveness, limiting intervention effectiveness.²² To address these challenges, modern EHR platforms incorporate tiered alert systems, adaptive thresholds, and customizable interfaces. AI tools must be embedded in practice, balancing sensitivity with cognitive load. AI alerts require robust pipelines with interoperability, low-latency streaming, and governance.²² Without resilient infrastructure, even accurate models may fail at the bedside.

Toward Precision Nephrology: Subphenotyping and Personalized AI

AKI is a heterogeneous syndrome, requiring an individualized approach in evaluation and management. Unsupervised learning identifies distinct AKI subtypes (hemodynamic, tubular, and sepsis related) with divergent trajectories and responses.^23,24 These findings support precision nephrology but require validation and integration.^25–28

AI in Nephrotoxin Surveillance and Drug Dosing

The utility of AI extends beyond detection to proactive risk mitigation. One critical application is the management of nephrotoxic medications—a modifiable and common cause of AKI.²⁹ AI has been leveraged in precision dosing to reduce nephrotoxicity risks.²⁹ Tootooni et al. developed an ML-based tool using extreme gradient boosting algorithms to predict vancomycin levels and guide dosing decisions.³⁰ This model demonstrated good discrimination for both subtherapeutic and supratherapeutic concentrations in critically ill patients. Such tools show promise, but adoption remains limited.³¹

Despite these advancements, clinician skepticism remains a barrier, particularly toward fully autonomous dosing systems. Surveys emphasize the importance of retaining human oversight to ensure safety, especially in high-acuity environments. AI tools must, therefore, be positioned as decision support systems that augment rather than replace clinical judgment.

AI To Predict Intradialytic Hypotension in Critical Care

AI and ML models have shown considerable potential in predicting intradialytic hypotension (IDH) within both critical care and hemodialysis settings.^32–39 Advanced ML, including recurrent neural networks and gradient boosting, can forecast IDH up to 60 minutes in advance with AUROCs >0.85.^40,41 Predictors include BP, ultrafiltration (UF), weight gain, and prior IDH.⁴⁰ Incorporating data from previous dialysis sessions has been shown to further enhance predictive performance.^32–39

AI dashboards provide real-time IDH alerts and support prevention,^40,41 but limited validation and heterogeneous methods impede adoption.⁴⁰

AI in CKRT

AI and ML models are increasingly being applied to enhance prognostication and resource management in CKRT.¹ Recent studies have demonstrated the feasibility of AI-driven outcome prediction in CKRT. Zamanzadeh et al. developed a data-driven model to predict survival outcomes in patients undergoing CKRT.⁴² Gu et al. used ML approaches to estimate both short-term and long-term mortality among patients with AKI after CKRT initiation, enabling earlier risk stratification and potentially guiding treatment intensity.⁴³ In pediatric populations, Li et al. introduced a transfer learning–based framework that improved outcome prediction for children with sepsis requiring CKRT, addressing the challenges of limited data availability in vulnerable subgroups.⁴⁴ AI in CKRT may improve prognostication, circuits, and compliance.¹

Post-AKI Care and Remote Monitoring

Remote patient monitoring has been proposed as a strategy to improve post-AKI care.^45,46 At Mayo Clinic, a program enrolling patients with stage 3 AKI involved daily vital sign monitoring and weekly laboratory testing, with predefined protocols for intervention. Preliminary data suggested fewer emergency department visits but no reduction in readmissions.^45,46 Additional studies are needed to clarify the benefits and cost-effectiveness of remote patient monitoring in this population.

Emerging AI Methodologies: Enhancing Trust and Actionability

Several advanced methodologic approaches hold particular promise for CCN but require careful contextualization for clinicians. Causal inference methods, including directed acyclic graphs and propensity-based frameworks, move beyond correlation to approximate causal effects, such as evaluating whether fluid overload directly worsens renal recovery in ICU patients.^47,48 RL extends this by enabling sequential decision making, where algorithms simulate dynamic adjustments (e.g., titrating UF rates in CKRT) on the basis of evolving patient responses and long-term reward signals such as survival or avoidance of hemodynamic instability. Conformal prediction offers a complementary safeguard by generating calibrated prediction intervals, allowing models to indicate when they are uncertain.^47–49 For example, an AKI prediction model may output a 70% risk estimate with a confidence band, prompting clinicians to weigh results alongside other clinical indicators rather than treating the output as an absolute. These methods help make AI more interpretable, reliable, and clinically aligned in CCN.

Quantifying Uncertainty: Conformal Prediction

Traditional ML/deep learning models output single risk scores without indicating confidence. Conformal prediction provides confidence intervals, improving transparency in high-stakes nephrology.^47,48

From Prediction to Causation: The Role of Causal Inference

Predictive models estimate outcome likelihood; causal inference models estimate how that likelihood changes if a specific action is taken. Techniques such as causal graphs, counterfactuals, and structural models simulate intervention effects, enabling more actionable recommendations (Table 2).⁴⁹ Causal models simulate how interventions (e.g., stopping vancomycin) alter AKI risk, guiding modifiable actions.

Table 2.

Comparison of predictive artificial intelligence models and causal inference approaches in critical care nephrology

Aspect	Prediction Model	Causal Inference Model
Primary question	What is the risk of an outcome?	What is the effect of an intervention on that outcome?
Use case	Estimate AKI risk on the basis of laboratory test results and vitals	Estimate risk reduction if vancomycin is discontinued
Output type	Risk scores, probabilities	Treatment effect estimates (e.g., risk difference or ratio)
Example output	Patient has 35% risk of AKI	Stopping vancomycin reduces AKI risk by 12%
Clinical role	Risk stratification, early warning	Decision making, individualized treatment planning
Methodology	ML/DL models (e.g., XGBoost and CNNs)	Causal graphs, counterfactual models, and inverse probability weighting
Data assumptions	Correlation suffices	Requires assumptions of exchangeability, no unmeasured confounding
Limitations	May reflect spurious correlations; not action guiding	Sensitive to model specification; requires domain expertise
Complementarity	Flags high-risk patients	Guides which actions might modify that risk

CNN, convolutional neural network; DL, deep learning; ML, machine learning; XGBoost, extreme gradient boosting.

Clinical Integration

Applied to CKRT, a standard model might recommend a fixed UF rate (250 ml/h), whereas a conformal predictor could offer a range (200–300 ml/h), signaling certainty and informing adjustments for unstable patients. Similarly, causal models could present projected outcome differences between continuing versus discontinuing a drug, integrated into EHRs. Together, causal inference and conformal prediction make AI outputs more transparent, actionable, and trustworthy.

Generative AI Applications in CCN

The emergence of generative AI, particularly large language models (LLMs), has introduced new horizons in AI applications in clinical documentation, reasoning, and patient communication.^1,50,51 Unlike traditional ML models, which are typically limited to classification or prediction tasks using structured inputs, LLMs can synthesize free text, multimodal data, and patient-specific context into coherent narratives and recommendations.

In nephrology, LLMs offer utility across a range of clinical functions. They can draft notes, summarize fluid data, and generate discharge instructions.^1,50,51 They may also support clinical decision making by simulating reasoning pathways, suggesting differential diagnoses, or formulating dialysis prescriptions on the basis of recent hemodynamic and laboratory data.⁵² As generative AI systems advance in CCN, their roles span a spectrum from clerical augmentation to autonomous therapeutic guidance.⁵² This evolution has implications for regulation, validation, and oversight. Figure 2 presents a conceptual maturity framework that organizes AI tools into four ascending levels, from passive documentation assistants to autonomous clinical agents. Understanding this hierarchy is essential for evaluating implementation feasibility, clinical risk, and governance requirements. Generative AI spans from clerical tasks to higher-risk reasoning, all of which require oversight.

Figure 2.

Hierarchy of AI integration in CCN. This diagram illustrates the progressive integration of AI tools in CCN, from basic documentation support to autonomous clinical agents. Levels of integration are aligned with increasing clinical autonomy and regulatory oversight, ranging from nonclinical documentation assistants to real-time autonomous systems for decision execution, such as RL-based dosing or CKRT titration. SaMD, Software as a Medical Device.

Although real-time ambient voice capture is restricted in ICU settings because of patient condition, generative AI remains highly applicable to other components of ICU documentation.⁵³ LLMs can summarize discussions during clinical rounds or synthesize structured and unstructured data, such as laboratory values, physiologic monitoring, imaging reports, and consult notes, into daily progress notes, multiday ICU summaries, and discharge documentation.⁵² Pilot implementations, such as ambient transcription systems and documentation summarizers, have been launched at institutions including Mayo Clinic and Yale New Haven Health.^54–56 Current LLMs have limited ability to interpret structured time-series data without augmentation via external parsing pipelines or domain-specific fine-tuning.^52,56 Nevertheless, CCN's data-rich and protocol-driven environment makes it an ideal candidate for additional LLM-enabled innovation.

Risks and Mitigation Strategies

A key concern with generative AI is hallucination, defined as generating text that is coherent but incorrect.⁵⁷ In nephrology, such errors may lead to inappropriate medication dosing, fluid management errors, or incorrect diagnoses. Reported hallucination rates in medical use cases vary widely, ranging from 2% to 15%, depending on the specific task and the model used.⁵⁷

Risk mitigation strategies^52,57 include (1) retrieval-augmented generation, which anchors AI responses in validated clinical resources (e.g., CKRT manuals); (2) chain-of-thought prompting, which forces the model to display its reasoning steps for clinician verification; (3) ensemble prompting, which uses multiple AI outputs to converge on a consensus recommendation; (4) data cross-validation, which compares AI outputs against structured patient data to detect inconsistencies; and (5) domain-specific fine-tuning, which improves accuracy using nephrology corpora, although at a risk of overfitting.

Even with safeguards (Figure 3), hallucination risk cannot be eliminated.^58,59 Generative AI should act as a clinical copilot, improving documentation and decision support under human oversight.

Figure 3.

Risk mitigation framework for generative AI in CCN. This schematic outlines key safeguards to reduce hallucination risk, including RAG, chain-of-thought prompting, ensemble prompting, data cross-validation, and domain-specific fine-tuning. These layers support safe, interpretable, and clinician-supervised deployment of generative AI tools. LLM, large language model; RAG, retrieval-augmented generation.

Real-World Implementations and Emerging Tools

Despite rapid advances, most AI tools for CCN remain in early deployment, with limited evidence of effect on patient-centered outcomes. Ambient AI scribes, such as Abridge, have been adopted at institutions including CHRISTUS Health, Mayo Clinic, and Yale New Haven Hospital, reducing documentation time and clinician burnout, although benefits are primarily operational.^60–62

In nephrology, generative AI has shown narrow utility, such as CKRT alarm troubleshooting with over 90% reported accuracy, and is being explored for simulated cases, training content, and adaptive teaching.^63–65 As illustrated in Figure 4, a generative AI system could collect transmembrane pressure, UF rates, laboratory results, and vasopressor requirements to generate structured narrative summaries. Upward transmembrane pressure trends could trigger alerts recommending evaluation for filter clotting. Such reports may support early detection of membrane dysfunction, guide UF adjustments, and enhance situational awareness, potentially reducing clinician burden during critical illness. This reflects a shift from clerical support to decision synthesis in CCN.

Figure 4.

Generative AI–enabled CKRT management. This schematic illustrates a generative AI system designed to optimize CKRT management by integrating data from the EHR and dialysis circuit parameters. The model analyzes trends such as increasing TMP and generates real-time clinical alerts to guide fluid removal or circuit assessment. Clinician input is incorporated into a continuous feedback loop to refine model recommendations and support adaptive learning. TMP, transmembrane pressure.

Specialized nephrology AI tools for AKI prediction, CKRT alarm triage, and RL-based dialysis management are largely in pilot stages (Table 3).^{30,60,63,66–74} Although the technical foundation is advancing, widespread adoption will require rigorous multicenter validation, seamless workflow integration, and evidence of improved patient outcomes.

Table 3.

Examples of specialized artificial intelligence tools in critical care nephrology

Tool Name	Developer Vendor	Domain-Intended Use Case	Validation Stage	Regulatory Status
DeepSOFA67	UF Health	ICU acuity scoring on the basis of continuous physiologic data	Retrospective analyses	Not FDA cleared
Vancomycin trough-level classification tool30	Mayo Clinic (academic/noncommercial)	AI-guided vancomycin dosing optimization in critically ill patients	Pilot studies	Not FDA cleared
Abridge60	Abridge AI, Inc.	Ambient AI scribe for clinical documentation	Deployed in real-world clinics	HIPAA compliant; not registered as SaMD
Sepsis Watch68	Duke Institute for Health Innovation	Early detection of sepsis using EHR data	Deployed in a single health system	Not FDA cleared
OpenEvidence69	OpenEvidence (participant in Mayo Clinic Platform_Accelerate program)	Generative AI for synthesizing medical literature and clinical guidelines across specialties to support evidence-based care	Early real-world implementation; feedback ongoing	Not FDA cleared; not classified as SaMD
Alert AKI70,71	University of Edinburgh/NHS Scotland	EHR-integrated AKI detection and alerting	Multicenter deployment in NHS hospitals	UK NHS certified; not FDA cleared
Nuance DAX72	Microsoft/Nuance	Ambient clinical documentation, speech to text	Commercially deployed	HIPAA compliant; not SaMD
Epic Sepsis Model73	Epic Systems Corp.	Sepsis risk prediction integrated into epic EHR	Deployed widely in hospitals	Not FDA cleared
Gideon74	GIDEON Informatics	Infectious disease differential diagnosis, occasionally relevant in nephrology (e.g., transplant infections)	Commercial product	Not FDA cleared for diagnostic use

AI, artificial intelligence; CVVHDF, continuous veno-venous hemodiafiltration; DAX, Dragon Ambient eXperience; DIHI, Duke Institute for Health Innovation; EHR, electronic health record; Epic, Epic Systems Corporation; FDA, US Food and Drug Administration; GIDEON, Global Infectious Diseases and Epidemiology Network; HIPAA, Health Insurance Portability and Accountability Act; ICU, intensive care unit; LLM, large language model; NHS, National Health Service; SaMD, Software as a Medical Device; UF Health, University of Florida Health.

Implementation Considerations

Institutional Readiness, Workflow, and Cost

Successful AI deployment in CCN requires infrastructure investment, governance structures, and clinician training, with AI champions bridging developers and end users. Early implementations have revealed challenges such as high false-positive rates from AKI alerts, leading to alert fatigue.^5,75 These issues can be mitigated through adaptive thresholds, stratified urgency, and embedded action prompts. Ambient AI scribes have reduced documentation burden but raised concerns over hallucinations and loss of nuance, reinforcing the need for human oversight.^75–77

Human-Centered Design

An important human factor in AI adoption is automation bias, the tendency to over-rely on automated outputs.^78,79 Automation bias includes omission (ignoring AI errors) and commission (acting on flawed outputs). To mitigate these risks, AI tools in CCN must be designed to encourage critical appraisal, with transparent outputs and explanations that promote informed clinical oversight rather than blind trust.⁸⁰ Equally central is the distinction between human-in-the-loop and human-on-the-loop paradigms. In the former, clinician approval is required before an AI-generated action (e.g., fluid removal adjustment) is executed, whereas in the latter, the AI may act autonomously with clinician override capability. The choice between these models carries significant regulatory and safety implications, particularly for high-stakes applications such as CKRT titration. Evaluation of human factors should use standardized frameworks. NASA Task Load Index quantifies workload,⁸¹ whereas alarm fatigue studies and Independent Ethics Committee 60601-1-8 provide benchmarks.^82,83 Incorporating these ensures that cognitive load, fatigue, and trust are assessed reproducibly, making usability evaluation more actionable.

Implementation Science

Implementation science offers a systematic framework to evaluate how AI-enabled tools perform in real-world practice.⁸⁴ Key domains include fidelity (how closely actual use aligns with intended design), adoption (extent of clinician uptake), penetration (integration across units or health systems), and sustainability (long-term use under routine conditions). Mixed methods and pragmatic trials capture both efficacy and effectiveness, providing insights into scalability.⁸⁴ Embedding these approaches will bridge the gap between validation and adoption.

Bias, Equity, and Representativeness

AI models can underperform in underrepresented populations, potentially worsening disparities in AKI severity, CKD progression, and transplant access.⁸⁵ Bias can be mitigated by evaluating subgroup-specific metrics by race, sex, and socioeconomic status and by using fairness constraints, reweighting methods, or federated learning to enhance representation while preserving privacy.

Generalizability and Transparency

Overcoming the so-called black box problem requires a deliberate focus on explainable AI.^86,87 Techniques such as SHapley Additive exPlanations, local interpretable model-agnostic explanations, and counterfactual reasoning move beyond abstract feature weights by providing case-level explanations of why a model generated a specific prediction.⁸⁸ For example, an AKI risk alert becomes more credible and actionable if the clinician can see that the risk is driven by rising creatinine, recent contrast exposure, and concurrent vancomycin administration. These methods are, therefore, not merely technical validation tools but central mechanisms for building clinical trust, promoting transparency, and enabling safe adoption of AI in CCN. Reframing interpretability in this way positions explainable AI as a key bridge between algorithmic performance and meaningful bedside decision making.

Data Governance and Sharing

Effective AI integration depends on standardized, interoperable data. Proprietary device formats, lack of application programming interfaces, and unclear ownership of machine-generated data hinder EHR integration and limit scalability. Federated learning offers a privacy-preserving alternative but requires harmonization of heterogeneous data. Addressing these barriers will require common data standards, transparent agreements, and coordinated governance frameworks. AI deployment also depends on robust data engineering. ICU datasets need feature engineering, imputation, and reconciliation of asynchronous data. Regular retraining with governance, audit logs, and change-control plans prevents model drift and maintains fairness and accuracy.

Practical solutions for data harmonization and interoperability are increasingly available and essential for scalable AI deployment in CCN. Common data models such as the Observational Medical Outcomes Partnership provide a standardized framework for structuring heterogeneous clinical data,⁸⁹ whereas interoperability standards like Fast Healthcare Interoperability Resources enable consistent exchange of patient information across systems.⁹⁰ For clinical integration, deployment pathways such as substitutable medical applications, reusable technologies on Fast Healthcare Interoperability Resources and clinical decision support Hooks have become de facto standards,⁹¹ allowing AI-driven decision support to be embedded directly into EHR workflows. These frameworks support secure data access, context-aware recommendations, and seamless user interfaces during clinical encounters. Incorporating such widely adopted standards not only facilitates implementation but also accelerates the translation of AI innovations into everyday practice.

Regulatory Landscape

The regulatory environment for AI in health care is evolving rapidly, with recent milestones that directly affect high-stakes applications in CCN. In the United States, the US Food and Drug Administration's finalized 2025 guidance on predetermined change control plans now provides a defined pathway for adaptive AI systems,⁹² allowing sponsors to prospectively specify how models may be updated while maintaining regulatory compliance. The European Union AI Act (2025) classifies health care AI as high risk, requiring transparency, risk management, and oversight.⁹³ These efforts are complemented by Good Machine Learning Practice principles, jointly advanced by international regulators, which emphasize reproducibility, transparency, and lifecycle monitoring.⁹⁴ Equally important are evolving standards for clinical trial design and reporting of AI interventions. The standard protocol items: recommendations for interventional trials–AI extension provides structured guidance for prospectively designing AI-focused clinical trials,⁹⁵ and the consolidated standards of reporting trials–AI extension defines best practices for transparent reporting of randomized evaluations of AI interventions.⁹⁶ Adoption of these frameworks enhances methodologic rigor, supports international harmonization, and builds trust among clinicians, regulators, and patients. Together, these regulatory and reporting standards lay the foundation for safe, reproducible, and ethically responsible deployment of AI in CCN.

Economic Considerations

Ambient AI may improve workflow, reduce EHR time, and increase billing capture.^{54,55,60,61,97,98} However, cost-effectiveness data in CCN are lacking, and most evidence is limited to surrogate outcomes. AI may reduce ICU stay, but this remains unproven; AKI alert trials improved processes, not outcomes or costs.^21,99–106 Rigorous, prospective cost-effectiveness studies are needed to define the true economic value of AI in CCN (Table 4).

Table 4.

Toolkit for evaluating artificial intelligence tools in critical care nephrology

Domain	Evaluation Prompt	Minimum Criteria	Example Indicator
Clinical relevance	Does the AI address a high-impact, nephrology-specific need?	Targets a defined clinical gap (e.g., AKI, IDH, and CKRT prescription)	Evidence of unmet need; mapped to clinical guidelines
Performance validity	Has the model been externally validated on diverse datasets?	AUROC ≥0.80; calibration curve reported (e.g., observed versus predicted risk); ≥1 external validation	Published multicenter studies with AUROC, AUPRC, calibration plots, and performance metrics across subgroups
Usability and workflow integration	Is the tool embedded in existing EHR workflows and usable during clinical decision making?	≤3 clicks to access output; <30 s review time; minimal alert fatigue	User satisfaction surveys, override rates, and dropout metrics
Interpretability	Can clinicians understand the reasoning behind AI recommendations?	SHAP values, feature attribution, or counterfactual explanation provided	The tool includes a visual interpretability interface
Safety and risk mitigation	Does the tool quantify uncertainty or limit automation in high-risk decisions?	Uses conformal prediction, flags high-uncertainty outputs, or enforces human in the loop	Coverage guarantees and flagged alerts for unsafe inputs
Equity and bias auditing	Has performance been stratified by sex, race, SES, and comorbidities?	Fairness metrics reported; subgroup AUC gaps <5%	Stratified AUCs and reweighting/fairness constraint documentation
Regulatory compliance	Is the tool cleared or documented under relevant regulatory frameworks?	Defined regulatory pathway (e.g., SaMD and PCCP); HIPAA/GDPR compliance	FDA SaMD clearance, EU AI risk classification, and internal audit logs
Economic feasibility	Does the cost of implementation justify gains in outcomes or efficiency?	ROI modeled; licensing <10% of QI budget; indirect cost quantified	Total cost of ownership, ROI projection, and implementation feasibility study
Postdeployment monitoring	Are there systems in place to detect model drift or performance degradation?	Real-time surveillance dashboards, retraining protocols, and override tracking	Monthly monitoring reports and data drift detection logs
Clinician feedback loop	Can clinicians provide structured feedback that informs future model refinement?	Embedded feedback option; updates are deployed quarterly	Feedback capture rate; release notes reflecting user suggestions

AI, artificial intelligence; AUC, area under the curve; AUPRC, area under the precision-recall curve; AUROC, area under the receiver operating characteristic curve; CKRT, continuous KRT; EHR, electronic health record; EU, European Union; FDA, US Food and Drug Administration; GDPR, general data protection regulation; HIPAA, Health Insurance Portability and Accountability Act; IDH, intradialytic hypotension; PCCP, predetermined change control plan; QI, quality improvement; ROI, return on investment; SaMD, Software as a Medical Device; SES, socioeconomic status; SHAP, SHapley Additive exPlanations.

AI-Enabled Quality Assurance

Another emerging role for AI in CCN is quality assurance.¹⁰⁷ AI-enabled monitoring systems can continuously analyze CKRT programs for adherence to institutional protocols, detect deviations in prescribed versus delivered dose, and identify patterns of frequent circuit clotting or downtime. Similarly, automated dashboards can benchmark unit-level performance, providing near real-time feedback to clinicians and administrators. These tools extend beyond individual patient care, serving as continuous audit mechanisms that enhance safety, optimize resource use, and support compliance with quality metrics in CCN.

Future Directions

The advancement of AI in CCN hinges not just on algorithmic performance but on successful translation into measurable clinical and societal outcomes. Progress requires coordinated efforts across clinical validation, workflow integration, longitudinal surveillance, ethical oversight, and governance (Figure 5). Federated learning, hybrid trial frameworks, and emerging agentic AI capabilities signal a future where scalable and accountable AI is possible. However, achieving this will depend on institutional readiness, stakeholder engagement, and rigorous postdeployment monitoring to ensure enduring safety, equity, and effectiveness.

Figure 5.

Strategic framework for responsible AI integration in CCN. This framework illustrates key domains, including clinical validation, ethics, governance, surveillance, collaboration, and economics, all of which are required for safe and equitable AI integration in CCN. Emerging paradigms such as federated learning, hybrid trials, and agentic AI highlight future directions for achieving scalable and accountable implementation. ROI, return on investment.

Although many of the applications discussed in this review, such as RL for dialysis optimization and generative AI for workflow augmentation, are promising, it is important to emphasize that most remain at a conceptual or early feasibility stage. Few trials exist, and real-world outcome studies are absent. Rigorous validation is needed before routine use, or performance may be overestimated and harms overlooked. Future work should prioritize pragmatic trials, external validation across health systems, and postdeployment surveillance to ensure that AI tools deliver measurable improvements in safety, efficiency, and outcomes in CCN.

AI should augment, not replace, clinical judgment. Meaningful integration in CCN requires tools that are interpretable, economically sustainable, and codesigned with frontline clinicians to align with real-world workflows. Current evidence suggests that clinicians do not substantially alter their decision-making processes despite the availability of these tools, which may contribute to their limited effect on clinical outcomes. Until technology matures, automation should be limited to safe, low-cost tasks such as automatic urinalysis ordering. Future research must prioritize pragmatic, multicenter trials that evaluate both patient-centered outcomes and system-level adoption, while explicitly addressing fairness across sex, race, and socioeconomic strata (Figure 6). As AI systems become increasingly autonomous, particularly in high-stakes applications such as dynamic CKRT titration, it is essential to establish robust regulatory frameworks, maintain human-in-the-loop safeguards, and implement adaptive governance. Ultimately, realizing the promise of AI in CCN will require aligning technical advancement with measurable clinical effect, institutional readiness, and sustained clinician trust.

Figure 6.

Roadmap for future implementation of AI in CCN. This framework highlights five priorities for scalable and ethical AI deployment: pragmatic trials, fairness audits, federated learning, regulatory clarity, and cost-effectiveness analysis. These pillars define a forward-looking strategy for safe and clinically meaningful implementation. CDSS, clinical decision support system; RCT, randomized controlled trial.

Conclusion

AI, spanning ML and generative models, holds transformative potential for CCN. From AKI prediction to KRT optimization and documentation, it offers more efficient care if guided by validation, oversight, and ethics.

Disclosures

Disclosure forms, as provided by each author, are available with the online version of the article at http://links.lww.com/KN9/B338.

Author Contributions

Conceptualization: Wisit Cheungpasitporn, Kianoush Kashani, Charat Thongprayoon.

Visualization: Wisit Cheungpasitporn, Charat Thongprayoon.

Writing – original draft: Wisit Cheungpasitporn, Charat Thongprayoon.

Writing – review & editing: Kianoush Kashani.

Funding

None.