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.