Artificial intelligence in upper gastrointestinal bleeding: Can machine learning predict endotherapy requirements?

Table 2 Evidence and efficacy of use of artificial intelligence in gastrointestinal bleed

Ref.	Primary focus	Data type	Study type	Study metrics	Key AI/ML method(s)	Comment
Raghareutai et al[31], 2025	Pre-endoscopy risk stratification	Demograph, clinical and lab values, n = 1389	Retrospective review of prospectively collected data, internal validation	AUROC 0.74 to predict endoscopic intervention (0.81 in validation test set)	linear discriminant analysis	Identify patients of AUGIB who need endoscopic intervention. Dynamic changes need multiple inputs of data
Shung et al[6], 2020	Pre-endoscopy risk stratification	Clinical and lab data, n = 1958	Retrospective, multicentre, external validation	ML vs GBS; AUROC 0.90 vs 0.87 (external validation) sensitivity 100%, specificity 26%	Gradient boosting machines (XGBoost)	The ML model could identify low-risk patients who can be safely discharged
He et al[20], 2024	Real-time endoscopic prediction	Endoscopic images training data n = 3868, internal; validation data, n = 834; external validation data, n = 521	Multicenter prospective; external validation	AUC 0.80 in the validation data set, accuracy of 91.2%	DCNN (image-based Forrest)	The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test
Bai et al[14], 2025	Post-GI bleed mortality in cirrhosis	Clinical and lab data, n = 2467 cirrhotics	Multicenter prospective, international internal validation, no external validation	AUC of 0.789 (up to 0.986 in LS-SVMR model)	LS-SVMR (least squares support vector machine regression)	CAGIB score is similar to CTP, MELD and MELD-Na for the prediction of mortality. No comparison with AIMS65, Rockall and GBS
Boros et al[8], 2025	Post-GI bleed mortality	EHR registry (Hungarian GI bleed registry), n = 1021	Retrospective, fivefold cross-validation	XGBoost and CatBoost AUC 0.79 vs 0.62 of GBS in GIB	XGBoost and CatBoost	CatBoost reached a sensitivity of 78% and a specificity of 74%

AUROC: Area under the receiver operating characteristic curves; AUGIB: Acute upper gastrointestinal bleed; ML: Machine learning; GBS: Glasgow-Blatchford score; XGBoost: Extreme gradient boosting; AUC: Area under the curve; DCNN: Deep convolutional neural networks; GI: Gastrointestinal; LS-SVMR: Least square support vector machine regression; CAGIB: Cirrhosis acute gastrointestinal bleeding; CTP: Child-Turcotte-Pugh; MELD: Model for end-stage liver disease; MELD-Na: Model for end-stage liver disease-sodium; AIMS65: Albumin, international normalized ratio, mental status, systolic blood pressure, age > 65 years; EHR: Electronic health record; CatBoost: Categorical boosting.