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

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.