Updates on the clinical use of artificial intelligence in upper gastrointestinal diseases: A comprehensive literature review

Table 5 Summary of literature for gastric cancer

Disease	Ref.	AI architecture	Training/validation cohort	Targeted endpoint	Key AI performance metrics	Performance compared to humans
Gastric cancer	[46]	Deep learning (ENDOANGEL-LD; CNN-based real-time lesion detection during white light endoscopy)	907 patients AI-first group; 905 patients routine-first group	Gastric neoplasm miss rate reduction	AI-first miss rate 61% vs routine-first 27.3% (relative risk 0.224, P = 0.015)	Routine (unassisted) endoscopy: Gastric neoplasm miss rate 273%; AI-assisted: 6.1% (P = 0.015; 77.6% relative reduction)
	[58]	Multimodal deep learning — incremental Supervised Contrastive Learning Model (iSCLM): ResNet-34 (CT features) + ResNet-18 with Graph Attention Network (pathology WSI features)	Development: 1208 patients; incremental: 795 patients; external test 1: 162 patients; external test 2: 222 patients; prospective: 132 patients	Prediction of neoadjuvant chemotherapy response	Prospective cohort: AUROC 0.846; external test 1: AUROC 0.866; external test 2: AUROC 0.876	N/A
	[59]	Deep learning (GRAPE; 3D noncontrast CT with pixel-level segmentation, morphometric and contextual radiological feature integration)	Development: 3470 GC + 3250 non-GC cases; internal validation: 1298 cases; external validation: 18160 cases; opportunistic screening: 78593 CT scans	Gastric cancer identification and screening	Internal validation AUC 0.970; External validation AUC 0.927	GRAPE outperformed radiologists: Sensitivity improved by 21.8%, specificity by 14.0%
	[60]	AI-driven biomarker discovery (ESGCmiRD; machine learning integrating miRNA expression patterns, network analysis, and regulatory modeling)	miRNA expression data from 10071 patients (GEO + TCGA databases); validated on GSE211692, TCGA-STAD, and institutional cohort	Early-stage GC detection	5-miRNA signature AUC: 0.986 (test set)	N/A
	[61]	CNN-based whole-slide image classification algorithm	Training: 2434 whole-slide images; prospective validation: 7440 biopsy specimens; pathologist impact study: 150 cases assessed by 6 pathologists	Classification of gastric biopsy pathology	AUROC 0.9790 (NFD vs positive); Epithelial tumors: Sensitivity 100%, specificity 97.49%; 52% reduction in review time	N/A

AUROC: Areas under the receiver-operating-characteristics curves; GEO: Gene Expression Omnibus; TCGA: Cancer Genome Atlas; AI: Artificial intelligence; AUC: Area under the curve; CNN: Convolutional neural network; CT: Computed tomography; GC: Gastric cancer; GRAPE: Gastric Cancer Risk Assessment Procedure with Artificial Intelligence; N/A: Not applicable; NFD: Negative for dysplasia.