Artificial intelligence in gastroenterology: Enhancing clinical practice, managing challenges and exploring future directions

Table 1 Overview of artificial intelligence applications in gastroenterology

Condition	AI application
Malignant and premalignant lesions	Enhances WLE, NBI, WATS, VLE, and I-scan imaging for detection; assists targeted biopsies; real-time feedback during endoscopy for quality control
Colorectal carcinoma	Automated histopathological image analysis & tumor grading; early metastases detection; personalizes treatment; assesses bowel prep quality
H. pylori infection	Detects H. pylori infection; “H. pylori AI-Clinician”; assists in novel eradication technique identification
Gastrointestinal bleeding	Predicts acute GIB episodes, outcomes, bleeding severity; predicts need for transfusion and mortality in ICU; identifies bleeding sources endoscopically
Irritable bowel syndrome	Diagnoses via bowel sound/acoustic analysis; differentiates subtypes; detects subtle mucosal changes; personalizes diet; uses adaptive feedback and symptom monitoring apps
Liver conditions	Detects fibrosis and steatosis via imaging; classifies focal lesions; predicts NASH/HCC progression and outcomes; identifies varices; predicts survival and transplant-free survival
Pancreatic conditions	Predicts severity and outcomes of acute pancreatitis; differentiates chronic/autoimmune pancreatitis; differentiates pancreatic cystic neoplasms; early detection and staging of pancreatic ductal adenocarcinoma
Pediatric conditions	Early diagnosis of biliary atresia, intussusception, eosinophilic esophagitis

AI: Artificial intelligence; WLE: White light endoscopy; NBI: Narrow band imaging; WATS: Wide area transepithelial sampling; VLE: Volumetric laser endomicroscopy; GIB: Gastrointestinal bleeding; ICU: Intensive care unit; NASH: Non-alcoholic steatohepatitis; HCC: Hepatocellular carcinoma; H. pylori: Helicobacter pylori.