Image analysis using artificial intelligence, through advances such as machine learning or deep learning, is increasingly being applied to medical imaging. There has been considerable interest in using deep learning to detect various gastrointestinal disorders based on WCE imaging.

Due to the rapid progression and poor prognosis of esophageal cancer (EC), early detection and diagnosis of esophageal cancer are of great value in improving the prognosis of patients. Currently, science has applied artificial intelligence in risk stratification of early esophageal cancer.

Due to the rapid progression and poor prognosis of esophageal cancer (EC), early detection and diagnosis of esophageal cancer are of great value in improving the prognosis of patients. Currently, science has applied artificial intelligence in the endoscopic method of diagnosing early esophageal cancer.

Computer-aided detection and diagnosis (CAD), thanks to newly developed improvements in artificial intelligence, will help increase the detection rate of adenomas and thus contribute to reducing cancer incidence, improving the effectiveness of colonoscopy in screening for important outcomes such as colorectal cancer-related mortality.

Computer-aided detection and diagnosis (CAD), thanks to newly developed improvements in artificial intelligence, will help increase the detection rate of adenomas and thus contribute to reducing cancer incidence, improving the effectiveness of colonoscopy in screening for important outcomes such as colorectal cancer-related mortality.

Recent advances in artificial intelligence (AI) may assist gastroenterologists in identifying areas of potential dysplasia in identified Barrett's esophagus and performing targeted biopsies, thereby reducing procedure time, sedation time, and patient risk while maximizing potential biopsy yield.

There has been recent progress in the development and testing of various AI and machine learning (ML) algorithms to improve the identification of dysplastic and malignant mucosa. The Role of Artificial Intelligence in Barrett's Esophagus (Part 1)

Gastric cancer (GC) ranks third in the major cause of death from malignancy worldwide, and gastrointestinal endoscopy (EGD) is considered the best diagnostic tool for early-stage cancer, and artificial intelligence plays an important role in the treatment of gastric cancer.

The application of artificial intelligence in the early diagnosis of esophageal cancer actively supports the treatment of this dangerous cancer. The following article will help you better understand artificial intelligence and its application in the diagnosis of Barrett's esophagus and esophageal cancer.

Artificial intelligence has the potential to be applied during colonoscopy to automatically detect colorectal polyps and differentiate between cancerous and noncancerous polyps, with the potential to improve the detection rate of adenomas, which varies widely among endoscopists performing colonoscopy.

Wireless capsule endoscopy (WCE) allows physicians to examine the gastrointestinal tract by transmitting images wirelessly from a disposable capsule to a data recorder. Although WCE is the least invasive endoscopic technique for diagnosing gastrointestinal disorders, interpreting a patient undergoing WCE requires considerable time and training.