Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges

Table 4 Role of computer-aided detection technology in endoscopy

Technology	Function	AI action	Clinical output	Impact	Evidence level	Primary limitation
CADe (detection)	Real-time lesion localization	Processes endoscopic video stream and automatically highlights suspicious regions with visual overlays	Increased detection rate	Prevents missed lesions (e.g., sessile serrated lesions, early gastric cancer), particularly for less experienced endoscopists	Clinical trial/commercial	High rate of false positives leading to endoscopist fatigue; potential for increased procedure time; lack of generalizability across diverse populations
CADx (diagnosis)	Real-Time lesion characterization	Analyzes morphological and vascular patterns of a detected lesion	Classification of lesions (adenomatous vs non-adenomatous) or activity grading in IBD	Facilitates "Resect and Discard" or "Diagnose and Leave" strategies, reducing biopsy costs and time	Clinical trial/commercial	Variability in accuracy based on polyp location (e.g., proximal vs distal colon); 'black box' nature limiting clinician trust; need for external validation
Integrated system	Informed therapeutic decision	Simultaneous use of CADe and CADx in a single, non-interruptive workflow	Precise treatment plan	Optimizes workflow efficiency and standardizes quality of care across different operators	Clinical trial/commercial	Seamless integration into diverse healthcare environments; high development/maintenance costs; 'deskilling' risk for endoscopists; unclear regulatory/ethical frameworks for AI-assisted decisions

IBD: Inflammatory bowel disease; CADe: Computer-aided detection; CADx: Computer-aided diagnosis; AI: Artificial intelligence.