Harnessing artificial intelligence in gastroenterology and hepatology: Current applications and future perspectives

Table 2 Illustrative examples of artificial intelligence applications across different stages of the patient journey in gastroenterology and hepatology

Stage of patient journey	Clinical context	Representative AI applications	Benefits/added value	Hype cycle stage	Ref.
Risk stratification and screening	Asymptomatic or high-risk individuals	Predictive modeling for NAFLD, HCC, CRC risk	Early identification of at-risk patients, targeted screening programs	Slope of enlightenment	[1]
		Polygenic/biomarker-based risk stratification using EHR and genomics
Diagnosis	Symptomatic presentation or incidental findings	AI-assisted polyp detection during colonoscopy (real-time CADe)	Increased diagnostic accuracy, real-time decision support, reduced miss rates of small/flat lesions	Plateau of productivity (for CADe in CRC); peak of inflated expectations (for capsule endoscopy CNNs)	[4,12,15]
		Image-based classification of liver lesions (CNNs)
		Capsule endoscopy with U-Net architectures
Staging and prognostication	Confirmed disease (IBD, cirrhosis, cancer)	Fibrosis staging via elastography DL	Improved risk assessment, personalized follow-up plans	Slope of enlightenment	[4,15]
		AI HCC recurrence risk prediction (random survival forests)
		Prognostic models (ML, MELD + AI)
Treatment planning	Therapeutic decision-making	AI-augmented MDT support for IBD biologics	Data-informed, individualized therapeutic pathways	Innovation trigger → early peak	Radiomics-based TACE prediction, AUC 0.78-0.85[36]
		RL models for drug sequencing
		Radiomics + ML for TACE suitability in HCC
Therapy monitoring	During pharmacologic, endoscopic, or surgical therapy	AI-based monitoring of treatment response (e.g., colectomy trends)	Dynamic tracking, early alerts, adaptive therapy modulation	Peak of inflated expectations	Colectomy prediction, AUROC 0.80-0.83[36]; NLP AE detection, recall 074-0.82[41,43]
		NLP for adverse event detection
Follow-up and surveillance	Post-therapy or remission phase	Predictive models for relapse in IBD	Enhanced vigilance, resource optimization, reduced recurrence risk	Slope of enlightenment	IBD relapse models, AUROC 0.79-0.82[20,22]
		Surveillance of HCC post-resection using ML
Patient engagement and education	Across all stages	AI chatbots for symptom triage	Empowered patients, improved adherence, scalable support	Peak of inflated expectations (for chatbots); innovation trigger (for advanced NLP coaching)	[34]
		Personalized education via NLP-based tools
		Digital coaching for diet/lifestyle adherence

AI: Artificial intelligence; NAFLD: Non-alcoholic fatty liver disease; HCC: Hepatocellular carcinoma; CRC: Colorectal cancer; CADe: Computer-aided detection; CNNs: Convolutional neural networks; IBD: Inflammatory bowel disease; DL: Deep learning; ML: Machine learning; MELD: Model for end-stage liver disease; MDT: Multidisciplinary team; TACE: Transarterial chemoembolization; AUC: Area under the curve; NLP: Natural language processing; AUROC: Area under the receiver operating characteristic curve; AE: Adverse event.