Role of artificial intelligence in gastric diseases

Table 1 Glossary of technical terms used in artificial intelligence applications for gastric diseases

Technical term	Simplified definition
CADe	AI systems that automatically identify and highlight abnormal areas during endoscopy
CADx	AI systems that classify detected lesions into diagnostic categories (e.g., benign vs malignant)
CNN	AI architecture designed to analyze visual information from endoscopic images
Edge computing	Processing AI calculations directly on local devices rather than remote servers, enabling real-time analysis
LLM	General-purpose AI systems like GPT-4 that can understand and generate human-like text
Multi-agent architectures	Systems where multiple specialized AI components work together to solve complex clinical problems
Context window	The amount of information (text, images) an AI model can analyze simultaneously
One-shot learning	AI’s ability to learn from a single example, reducing the need for large training datasets

AI: Artificial intelligence; CADe: Computer-aided detection; CADx: Computer-aided diagnosis; CNN: Convolutional neural network; LLM: Large language model.

Table 2 Representative systematic reviews and meta-analyses of artificial intelligence applications in gastric diseases (2019-2025)

Ref.	AI application	Included studies (n)	Patients/images	Performance metrics	Key findings
For gastric cancer detection or diagnosis
Xie et al[38]	CNN for diagnosis and invasion depth prediction in gastric cancer	17	5539/51446	Sensitivity of 89%, specificity of 93%, AUC of 0.94	AI comparable to experts, superior to non-experts
Shi et al[39]	ML for image-based identification of EGC	21	Not specified	Sensitivity of 90%, specificity of 90%, SROC of 0.96	AI comparable to experts, superior to the combined group of expert and non-expert endoscopists
Jiang et al[40]	AI for endoscopic detection and invasion depth prediction of EGC	16	1708519 images/22621 EGC images	Sensitivity of 86%, specificity of 93%, AUC of 0.96 (detection); sensitivity of 72%, specificity of 79%, AUC of 0.82, (invasion depth diagnosis)	AI-assisted EGC diagnosis was more accurate than experts
Klang et al[41]	CNN for detection and diagnosis of gastric cancer	42	Not specified	Not specified	AI models frequently matched or outperformed human endoscopists in diagnostic accuracy
For diagnosis of H. pylori infection in endoscopic images
Bang et al[2]	Diagnostic test accuracy of AI for the prediction of H. pylori infection using endoscopic images	8	1719/2855 endoscopic images with H. pylori infection and 2287 control images	Sensitivity of 87%, specificity of 86%, AUC of 0.92, DOR of 40	An AI algorithm is a reliable tool for endoscopic diagnosis of H. pylori infection
Mohan et al[42]	CNN-based AI in the diagnosis of H. pylori infection	5	3558/10151 images	Sensitivity of 86.3%, specificity of 87.1%, accuracy of 87.1% (AI); sensitivity of 79.6%, specificity of 83.8%, accuracy of 82.9% (endoscopists)	AI model outperformed human endoscopists
Dilaghi et al[43]	AI in the diagnosis of gastric precancerous lesions and H. pylori infection	9	2430	Accuracy of 79.6%	AI-system seems to be a good resource for an easier diagnosis of H. pylori infection
Parkash et al[44]	Diagnostic accuracy of AI algorithms for detecting H. pylori infection using endoscopic images	11	Over 6122/over 107466	Sensitivity of 93%, specificity of 92%, accuracy of 92%	AI had high diagnostic accuracy for detecting H. pylori infection using endoscopic images
Jiang et al[45]	Diagnostic performance of AI based on endoscopy for detecting H. pylori infection	16	25002 images or patients	Sensitivity of 91%, specificity of 94%, accuracy of 98%	AI demonstrates higher diagnostic performance compared to both novices and senior endoscopists
For diagnosis of gastric precancerous lesions in endoscopic images
Li et al[47]	AI’s diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy	12	11173	Sensitivity of 94%, specificity of 93%, accuracy of 97%	AI exhibited a higher diagnostic capacity than endoscopists
Shi et al[46]	Accuracy of AI-assisted diagnosis of gastric atrophy	8	25216/84678	Sensitivity of 94%, specificity of 96%, AUC of 0.98	The accuracy of AI in diagnosing chronic atrophic gastritis was significantly higher than that of endoscopists
For diagnosis of invasion depth in gastric cancer
Xie et al[38]	CNN for diagnosis and invasion depth prediction in gastric cancer	17	5539/51446	Sensitivity of 82%, specificity of 90%, AUC of 0.90	AI comparable to experts, superior to non-experts
For prediction of lymph node metastasis in gastric cancer using clinical data
Li et al[50]	Diagnostic performance of ML in predicting lymph node metastasis in patients with gastric cancer	41	56182	Accuracy of 75.3%	ML has shown to be of excellent diagnostic performance in predicting the lymph node metastasis of gastric cancer

AI: Artificial intelligence; CNN: Convolutional neural network; AUC: Area under the curve; ML: Machine learning; SROC: Summarized receiver operator characteristic; EGC: Early gastric cancer; DOR: Diagnostic odds ratio; H. pylori: Helicobacter pylori.