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

Table 5 Evidence levels across major artificial intelligence applications in gastroenterology and hepatology

Domain/application	Evidence level	Key determinants
Endoscopy (CADe/CADx)	High	Multiple FDA/CE-approved tools; prospective multicenter trials; real-time clinical use
Radiology (CT/MRI)	Moderate-high	External validation common; some multicenter cohorts; radiomics + DL pipelines
Non-invasive liver tests/fibrosis	Moderate	Mix of large cohorts + retrospective datasets; limited external validation except for FIB-6
HCC detection and surveillance	Low-moderate	Early-stage models; heterogeneous metrics; mostly retrospective; few external validations
IBD (imaging, histology)	Moderate	Prospective validation for endoscopy/histology models; omics models still experimental
IBD multi-omics/transcriptomics	Low	Experimental; small cohorts; no external validation
Capsule endoscopy	Moderate	Strong DL performance; mostly single-center retrospective datasets
Motility testing/manometry	Low-moderate	Emerging field; small datasets; experimental DL approaches
Predictive models for complications	Low-moderate	Mostly retrospective; internal validation only
Implementation/regulation	-	Not applicable (conceptual section)

IBD: Inflammatory bowel disease; CADe: Computer-aided detection; CADx: Computer-aided diagnosis; CT: Computerized tomography; MRI: Magnetic resonance imaging; DL: Deep learning; CE: Capsule endoscopy; HCC: Hepatocellular carcinoma.