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

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.