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

Table 2 Summary of literature for gastroesophageal reflux disease

Disease	Ref.	AI architecture	Training/validation cohort	Targeted endpoint	Key AI performance metrics	Performance compared to humans
GERD	[21]	ANN with TWIST evolutionary optimization (symptom questionnaire-based)	577 GERD patients and 94 healthy controls; QUID 41-item questionnaire data	Diagnosis of GERD from symptoms	Sensitivity 99.1%, specificity 99.8%, accuracy 99.2%	N/A
	[22]	ANN, decision trees, and logistic regression (data mining on 15-item symptom questionnaire)	132 patients with upper GI symptoms; 15-item diagnostic questionnaire	Diagnosis of GERD from symptoms	Sensitivity 70%-75%, specificity 63%-78%, AUC 0.787	N/A
	[23]	Large language model (ChatGPT, version March 14, 2023)	23 prompts × 3 submissions = 69 total queries; rated by 3 gastroenterologists and 8 patients	GERD management recommendations	91.3% appropriate responses (29.0% completely, 62.3% mostly); 78.3% contained specific guidance; 70% of prompts yielded inconsistent appropriateness across repeats	N/A
	[25]	Deep learning (DenseNet-121) with explainable AI (XAI)	2081 endoscopic images	LA-grade classification of GERD	AUC 0.968, classification accuracy 86.7%	Junior endoscopists: Accuracy 71.5%; experienced endoscopists: Accuracy 77.4% (DL model significantly higher)
	[26]	DL + CNN (GERD-VGGNet; VGG16 pretrained on ImageNet with transfer learning)	Development set: 464 cases (247 LA A-B, 155 LA C-D, 62 normal in conventional mode; augmented to 603000 training images); Test set: 32 images	LA-grade classification of GERD	Sensitivity 99.8%, specificity 99.7%, overall accuracy 98.9%	NBI test set — trainee 1 overall accuracy 75.0%; trainee 2 overall accuracy 65.6% (AI vs trainee 2 significant at 95% confidence)
	[27]	Two-stage: DL (Gerd-VGGNet/VGG16) for feature extraction + machine learning (Random Forest) classifier (GerdNet-RF)	Development set: 671 NBI images (244 LA A-B, 229 LA C-D, 198 normal); Test set: 32 images (12 LA A-B, 10 LA C-D, 10 normal)	LA-grade classification of GERD	Test set accuracy 92.5%, validation set accuracy 98.4%	N/A
	[28]	Prototype AI software for AIBI from pH-impedance tracings	325 subjects (117 off therapy, 93 on therapy, 115 asymptomatic controls)	Prediction of GERD symptom improvement	AIBI ratio AUC 0.661	N/A
	[29]	AI/machine learning with deep residual network (ResNet) for image recognition of impedance-pH tracings	106 patients; 7939 impedance events	Automated reflux episode and PSPW detection	Reflux episode accuracy 87%; PSPW accuracy 82%	N/A

ANN: Artificial Neural Network; AI: Artificial intelligence; GERD: Gastroesophageal reflux disease; N/A: Not applicable; AUC: Area under the curve; DL: Deep learning; CNN: Convolutional neural network; LA: Los Angeles; NBI: Narrow-band imaging; AIBI: Automated AI extraction of baseline impedance; PSPW: Postreflux swallow-induced peristaltic wave.