Synergistic applications of artificial intelligence and organoid technology in gastric precancerous lesion research: Mechanisms, translation, and challenges

Table 2 Role of artificial intelligence in reducing inter-observer variability and improving diagnostic consistency

No.	Research focus	Ref.	Modality	Study design	Sample size/dataset	AI model/system	Key performance metrics	Results without AI	Results with AI assistance	Key finding
1	EGC diagnosis with ME	Li et al[42]	Magnifying image-enhanced endoscopy	Diagnostic study	Not specified	ENDOANGEL-LA	Accuracy	Novice: 71.63%	Novice: 87.45%	AI assistance bridged the gap between novices and experts
2	Real-time AI assistance	Dong et al[43]	WLE	Diagnostic study	Not specified	ENDOANGEL-ED	Accuracy	Endoscopists: 70.61%	Endoscopists: 79.63% (P < 0.001)	AI significantly improved endoscopist diagnostic accuracy
3	CAG diagnosis	Zhao et al[45]	Not specified	Prospective nested case-control	1306 patients	Not specified	Kappa, accuracy, sensitivity, specificity	Endoscopists' kappa: 0.291; Acc: 68.89%; Sens: 67.56%; Spec: 70.23%	AI kappa: 0.816; Acc: 89.89%; Sens: 89.31%; Spec: 90.46%	AI agreement with pathology was substantially higher
4	Pathology Dx (atrophy/IM)	Fang et al[46]	Pathology slides	Observer study (10 pathologists)	Not specified	GasMIL (SDL algorithm)	AUC, weighted kappa	Pathologists’ performance (baseline)	Pathologists’ performance significantly improved	AI assistance improved pathologists’ diagnostic metrics

“Not Specified” indicates that the information was not explicitly mentioned in the provided text. AI: Artificial intelligence; CAG: Chronic atrophic gastritis; Dx: Diagnosis; IM: Intestinal metaplasia; ME: Magnifying endoscopy; SDL: Semi-supervised deep learning; Acc: Accuracy; Sens: Sensitivity; Spec: Specificity; AUC: Area under the curves; WLE: White light endoscope.