Table 1.

Applications of AI in diagnosis, prediction, and treatment with selected examples in nephropathy and other diseases

Scenario	Examples	AI techniques	Methods	Type of disease
Diagnosis	Multiclass segmentation of digitized kidney tissue sections [18]	Deep learning	CNN	Kidney disease
	Segmentation and classification of diabetic glomerulosclerosis [19]	Deep learning and unsupervised learning	CNN, unsupervised methods	Kidney disease
	Classification of skin lesions and skin cancer with images [3]	Deep learning	CNN	Cancer
	Detecting cancer metastases on breast cancer pathological images [4]	Deep learning	CNN	Cancer
Prediction	ESRD prediction for IgAN patients [16]	Supervised learning	XGBoost algorithm	Kidney disease
	Continuous prediction of AKI [20]	Deep learning	RNN	Kidney disease
	Prediction of all-cause mortality in patients with coronary artery disease [8]	Supervised learning	LogitBoost algorithm	Cardiovascular disease
	Outcome prediction for lymphoma with gene-expression profiling [7]	Supervised learning	Weighted voting algorithm	Cancer
Treatment and patient care	Recommendation of anemia therapy in hemodialysis patients [22]	Deep learning	Reinforcement learning	Kidney disease
	Clinical decision support for anemia management in hemodialysis patients [23]	Deep learning	ANN	Kidney disease
	Making referral recommendation in retinal disease [11]	Deep learning	ANN	Retinal disease
	Treatment recommendation for sepsis in intensive care [10]	Deep learning	Reinforcement learning	Intensive care