Artificial intelligence has the potential to be applied during colonoscopy to automatically detect colorectal polyps and differentiate between cancerous and non-cancerous polyps, with the potential to improve the detection rate of adenomas, which varies widely among endoscopists when performing colonoscopy. Currently, there are many H. pylori testing methods applied in clinical and research settings. Depending on whether the method requires a gastroduodenoscopy or not, it is divided into two groups: invasive methods and non-invasive methods.

AI-assisted diagnosis for upper gastrointestinal endoscopy is increasingly gaining attention for the automatic and accurate identification of dysplasia in Barrett's esophagus, as well as for the early detection of gastric cancers (GCs), thus preventing esophageal and gastric malignancies.

The demand for more effective methods of detecting early esophageal cancer features has led to intensive research in the field of artificial intelligence (AI).

The demand for more effective methods of detecting early esophageal cancer features has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in image, video and other aspects of processing, while complex neural networks (CNNs) have paved the way for detect high-resolution endoscopic images and videos.

The application of artificial intelligence in Barrett's esophagus dysplasia and early esophageal adenocarcinoma has always been evaluated to bring high efficiency in diagnosis. As a result, the treatment becomes more precise and effective. Abbreviation: EC: Esophageal cancer AI: artificial intelligence Barrett esophagus (BE)

In order to provide accurate assessment and positive treatment direction, bringing high efficiency to patients, currently artificial intelligence techniques in optical tomography combined endoscopy and laser endoscopy have been applied. used in the diagnosis of esophageal cancer.