Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies

Table 1 Current diagnostic limitations in chronic kidney disease and key quantitative evidence

Diagnostic domain	Limitation	Key quantitative findings	Ref.
eGFR estimation	Bias and variability across equations	CKD-EPI 2021 mean bias -3.9 mL/minute/1.73 m² vs -0.5 mL/minute/1.73 m² (prior equation)	[14]
CKD awareness	High rates of undiagnosed disease	Stage-3 unawareness 70%-90%; > 70% lack diagnostic code	[17,18]
Stage-specific awareness	Late recognition in early stages	Approximately 80% (stages 1-2), approximately 71% (3a), approximately 49% (3b), >30% (stage 4)	[19]
Data integration	Fragmented multimodal data	Incomplete lab/imaging capture; limited NLP use	[20,21]

Bias values are expressed relative to measured glomerular filtration rate and reported in mL/minute/1.73 m². eGFR: Estimated glomerular filtration rate; CKD-EPI: Chronic kidney disease epidemiology collaboration; CKD: Chronic kidney disease; NLP: Natural language processing.

Table 2 Major artificial intelligence model categories for chronic kidney disease detection and early risk stratification

Model category	Data source	Typical task	Key performance range	Primary clinical use	Ref.
Clinical early-warning	EHR, claims, labs	6-12 months incident CKD prediction	AUROC 0.80-0.95	Trigger confirmatory testing/referral	[25,26]
Multimarker ML	Creatinine + cystatin C + labs	Threshold reclassification	↓ False negatives near cutoffs	Reduce misclassification	[16,29]
Imaging-based DL	CT, ultrasound	Structural injury detection	Dice 81%-94%; Acc 86%-90%	Noninvasive fibrosis/triage	[30,31]
Population screening	Registries, insurance	Risk stratification	AUROC 0.80-0.95	Targeted screening	[33]

Performance ranges reflect external validation when reported. CKD: Chronic kidney disease; CT: Computed tomography; AUROC: Area under the receiver operating characteristic curve; ML: Machine learning; DL: Deep learning.

Table 3 Survival modeling approaches for kidney failure prediction and comparative performance

Model type	Data inputs	Competing risks handled	Typical performance	Comparator	Key advantage	Ref.
Traditional Cox	Baseline labs	Rarely	C-index 0.65-0.75	KFRE	Simplicity	[39]
KFRE	Static clinical variables	No	C-index 0.70-0.80		Widely adopted	[39]
ML survival (RSF, DeepSurv)	Longitudinal labs	Yes	C-index 0.75-0.88	Cox, KFRE	Dynamic risk	[40]
Multimodal ML	Labs + imaging ± genomics	Yes	+0.03-0.07 C-index gain	Lab-only ML	Rapid progressor ID	[46]

Reported performance ranges are study-level estimates derived from independent validation cohorts. KFRE: Kidney failure risk equation; ML: Machine learning; RSF: Random survival forest.

Table 4 Clinical decision-support applications of artificial intelligence across chronic kidney disease treatment domains

Treatment domain	Clinical task	AI role	Performance range	Decision-support vs automation	Ref.
Pharmacotherapy	Nephrotoxicity risk	Monitoring and dose support	AUROC 0.72-0.86	Decision support	[52,53]
RAAS therapy	Hyperkalemia prediction	Monitoring intensity	AUROC 0.72-0.86	Decision support	[56]
Dialysis planning	Timing and access planning	Risk stratification	Improved time-dependent C-index	Decision support	[57]
Intradialytic care	IDH prediction	Preemptive adjustment	Sens. 0.78-0.88	Conditional automation	[58]
Peritoneal dialysis	Peritonitis risk	Surveillance tailoring	AUROC 0.75-0.88	Decision support	[61]

“Conditional automation” denotes algorithm-initiated actions requiring clinician confirmation. AI: Artificial intelligence; RAAS: Renin-angiotensin-aldosterone system; IDH: Intradialytic hypotension; AUROC: Area under the receiver operating characteristic curve.

Table 5 Artificial intelligence approaches for prediction and management of major chronic kidney disease complications

Complication domain	Clinical task	AI approach	Key performance	Care model	Ref.
Cardiovascular	Major CVD event prediction	XGBoost, radiomics	AUC 0.89; ΔAUC 0.02-0.07	Dynamic monitoring	[67]
Anemia	ESA dose support	ML, RL	Non-inferior Hb control; ↓ variability	Closed-loop support	[70]
CKD-MBD	PTH classification	XGBoost	AUROC 0.92	Closed-loop support	[69]
CKD-MBD	Multitarget dosing	RL (simulation)	Improved target attainment	Simulation-based	[73]

CKD-MBD: Chronic kidney disease-mineral and bone disorder; AUROC: Area under the receiver operating characteristic curve; ΔAUC: Change in area under the curve; PTH: Parathyroid hormone; ESA: Erythropoiesis-stimulating agent; ML: Machine learning; RL: Reinforcement learning; CVD: Cardiovascular disease; AI: Artificial intelligence; AUC: Area under the curve.

Table 6 Applications of artificial intelligence across the kidney transplantation continuum

Phase	Clinical task	AI approach	Key performance	Clinical role	Ref.
Pre-transplant	Allocation and offer ranking	ML survival, ranking models	C-index 0.63-0.79	Decision support	[77]
Pre-transplant	Immunologic risk	Eplet mismatch ML	Risk reclassification	Equity-aware support	[78]
Peri-transplant	Biopsy assessment	DL (WSI)	AUC up to 0.94	Diagnostic support	[82]
Post-transplant	Tacrolimus dosing	LSTM, PK-ML	↓ Dosing error	Dose support	[86]
Post-transplant	Remote monitoring	Wearables + ML	Improved adherence	Surveillance support	[90]

LSTM: Long short-term memory; PK-ML: Pharmacokinetic-machine learning; DL (WSI): Deep learning applied to whole-slide images; AI: Artificial intelligence; ML: Machine learning; AUC: Area under the curve.

Table 7 Ethical, methodological, and implementation principles for artificial intelligence deployment in chronic kidney disease care

Principle	CKD-specific risk	Quantitative evidence	Mitigation
Equity-aware design	Delayed referral, under-monitoring	17.7%→46.5% care reclassification after bias correction	Fairness audits, proxy review
Calibration	Misallocation of resources	KFRE miscalibration on external validation	Recalibration, subgroup testing
Explainability	Hidden proxy reliance	30%-60% studies include XAI	SHAP/LIME, clinician oversight
Privacy and integration	Limited deployment	0%-5% FL accuracy gap; 40%-60% integration cost	Federated learning, FHIR/CDS hooks

KFRE: Kidney failure risk equation; SHAP/LIME: SHapley Additive exPlanations/Local interpretable model-agnostic explanations; FHIR/CDS: Fast healthcare interoperability resource/clinical decision support; FL: Federated learning; CKD: Chronic kidney disease; XAI: Explainable artificial intelligence.

Table 8 Key barriers to clinical implementation of artificial intelligence in chronic kidney disease and proposed mitigation strategies

Barrier category	Failure mechanism	Quantitative evidence	Mitigation
Technical	Overfitting, drift, miscalibration	AUROC drop 0.05-0.15 on external validation	Multisite validation, recalibration
Organizational	Poor EHR/workflow integration	40%-60% of deployment cost/time	FHIR-based integration, co-design
Regulatory	Extended approval timelines	Months-years added to development	Early regulatory planning
LMIC context	Infrastructure and data gaps	Limited cost-effectiveness evidence	Local retraining, staged rollout

AUROC: Area under the receiver operating characteristic curve; LMIC: Low- and middle-income countries; EHR: Electronic health records; FHIR: Fast healthcare interoperability resources.

Table 9 Anticipated future directions of artificial intelligence in chronic kidney disease care across time horizons

Time horizon	Direction	Evidence base	Primary clinical impact
5 years	Multimodal foundation models	Rapid growth in medical multimodal AI[125]	Improved calibration and consistency
	Wearables and continuous monitoring	Digital CKD health reviews[126]	Earlier detection between visits
	PRO and text integration	41% of CKD AI studies use unstructured data[7]	Patient-centered decision support
≥ 10 years	Digital twins	Early CKD prototype models[7]	In silico therapy planning
	Multi-omics AI	Precision medicine frameworks[127]	Target discovery and stratification

PRO: Patient-reported outcomes; CKD: Chronic kidney disease; AI: Artificial intelligence.