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

Table 3 Relevant applications of artificial intelligence in inflammatory bowel disease

Application area	Data source and type	AI application/technique	Key clinical benefit	Evidence level	Primary limitation
Non-invasive diagnosis	Fecal multi-omics	ML models	Accurate differentiation of healthy vs UC vs CD	Research/early clinical	Data heterogeneity, limited external validation, small/retrospective datasets, lack of generalizability
Differential diagnosis	Endoscopic imaging and clinical records (NLP)	TextCNN/image analysis	Distinguishes CD from intestinal tuberculosis and UC from CD	Research	Symptoms overlap and endoscopic similarities; limited data quality; need for external validation
Disease assessment	Radiomics and endoscopic video	DL models	Quantifies inflammation, detects strictures, and automates endoscopic activity scoring improving standardization	Research	Limited data quality and standardization; lack of external validation; 'black box' nature
Histological prediction	Histopathological slides	CNNs/DL (automating RHI, NHI, PHRI)	Objective scoring and superior prediction of future flares and post-surgical recurrence	Research	High inter- and intra-observer variability in expert labeling; data quality; 'black box' interpretability
Therapeutic response	Clinical, laboratory, multi-omics, endoscopy data	ML predictive models	Predicts response to biologic treatment, enabling timely therapy adjustment	Research/early clinical	Disease heterogeneity; variable treatment responses; need for robust external validation; 'black box' interpretability
Risk stratification	Peripheral blood transcriptomics, histology	ML/DL models (low vs high-risk groups)	Predicts disease progression, need for treatment escalation, and post-surgical recurrence/complications (strictures/fistulas). Recurrence/complications (strictures/fistulas)	Research	Need for larger, diverse datasets; limited external validation; potential algorithmic bias
Drug development	Transcriptomic data (intestinal tissue)/Boolean networks	Identification of novel therapeutic targets	Accelerates the discovery of first-in-class therapies with novel mechanisms of action	Research	Complexity of biological systems; need for robust preclinical validation; 'black box' nature; ethical considerations
Clinical trials	Patient electronic medical records	NLP/DL algorithms	Accelerates patient recruitment and potential use of "Digital Twins"	Research/future prospect	Data privacy and security; regulatory frameworks; ethical concerns regarding "digital twins" and placebo groups

UC: Ulcerative colitis; CD: Crohn’s disease; NLP: Natural language processing; DL: Deep learning; CNN: Convolutional neural networks; RHI: Robarts Histopathology Index; NHI: Nancy Histological Index; PHRI: PICaSSO Histologic Remission Index; AI: Artificial intelligence; ML: Machine learning.