Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice

Table 1 Recent advances in machine learning applications for gastrointestinal diseases (2022-2025)[21-51]

AI algorithm	Parameters employed/study design	Sample size/control group/ validation	Outcomes	Performance	Ref.
SVM	Multi-center data + TCGA validation	Total n = 255 (training 212 + internal validation 43); external: 4 centers + TCGA	OS/DFS risk stratification (low/moderate/high); high-risk stage II/III chemotherapy benefit	AUC training 0.773 (OS)/0.751 (DFS); validation 0.852 (OS)/0.837 (DFS)	Li et al[21]
SVM + APINet/TransFG	Tongue features (color/morphology/coating) + microbiome (16S rDNA); multicenter prospective study	Cohort 1: GC = 328 vs NGC = 304; cohort 2: GC = 937 vs NGC = 1911 (10 centers); external: GC = 294 vs NGC = 521 (7 centers)	Distinguish GC/early GC/precancerous lesions (e.g., AG); superior to 8 blood biomarkers	Tongue model AUC: 0.89 (initial); 0.88-0.92 (internal); 0.83-0.88 (external); microbiome AUC: 0.94 (genus)/0.95 (species)	Yuan et al[22]
SVM/LR/kNN + feature selection	Liver/PBMC RNA-seq data	Liver = 67; PBMC = 137; external public dataset; controls: Healthy + AH/AC/MASLD/HCV	Precise differentiation of AH/AC/MASLD/HCV; minimal gene sets (33-75 genes)	Liver accuracy: 90% (AH/AC vs healthy), 91% (4-class); External 82%; PBMC accuracy: 75% (4-class)	Listopad et al[23]
SVM/LR/RF	Multiphase CT radiomics (n = 851)	Total n = 215 (training 150 + external 65)	Multiphase CT prediction (plain scan alternative)	Nomogram C-index 0.913 (95%CI: 0.878-0.956)	Liu et al[24]
SVM	Radiomics features extracted from CT images; integrated rad-score + clinicopathological characteristics	693 GC patients (2 centers); training (n = 390), internal validation (n = 151), external validation (n = 152) cohorts	Rad-scores significantly associated with diffuse-type GC and SRCC (P < 0.001)	Lauren nomogram: AUC = 0.895 (training), 0.841 (internal), 0.893 (external). SRCC nomogram: AUC = 0.905 (training), 0.845 (internal), 0.918 (external)	Chen et al[25]
Counterfactual random forest + optimal policy trees	Imatinib duration inferred via counterfactual model; OPTs interpreted counterfactual predictions	Internal: 117 (MSKCC); external: 363 (polish) + 239 (spanish)	OPTs recommended no imatinib for low-risk subgroups: Gastric GIST < 15.9 cm + mitotic count < 11.5/5 mm². Any site GIST < 5.4 cm + mitotic count < 11.5/5 mm²	Sensitivity: 92.7% (internal), 95.4% (Spanish), 92.4% (Polish). Specificity: 33.9% (internal)	Bertsimas et al[26]
Markov decision tree model	Input variables from systematic review/meta-analysis of RCTs comparing DS, EUS-GE, and GJ; prospective cohort study for EUS-GE	15 studies in Markov model	1-month survival: DS (81.2%), EUS-GE (80.4%) > GJ (75.5%). 6-month survival: GJ (25.2%), EUS-GE (23.8%) > DS (21.3%)	EUS-GE and GJ outperformed DS for long-term palliation (6 months)	Chue et al[27]
Decision trees, LASSO, kNN, random forests	Pathomics features extracted from HE-stained WSIs; multicenter retrospective study	584 gastric cancer patients (training: 325, internal validation: 113, external validation 1:73, external validation 2:73)	Pathomics signature independently predicted progression-free survival (P < 0.001, HR = 0.34)	Training: AUC = 0.985; Internal validation: AUC = 0.921	Han et al[28]
Optimal classification trees	Input variables: Tumour size, mitotic count, tumour site	Internal: 395 patients (MSKCC + Spanish consortium); external: 556 patients (polish registry)	OCT significantly improved calibration compared to MSK nomogram	Higher C-index for OCT (0.805 vs 0.788); slope = 1.041 (OCT) vs 0.681 (MSK); no significant calibration error for OCT	Bertsimas et al[29]
Gradient-boosting decision tree	Baseline characteristics, endoscopic atrophy	Total: 1099 chronic gastritis patients, training: 879, test: 220	Key predictors: Age, OLGIM/OLGA stage, endoscopic atrophy, history of other malignancies	Harrell’s c-index: 0.84 (test set). Stratified risk into 3 categories (P < 0.001)	Arai et al[30]
GBM	Prospective cohort study (15-year follow-up); 70% training and 30% validation split	FINRISK 2002 cohort: 7115 individuals (103 incident liver disease, 41 alcoholic liver disease)	Gut microbiome and conventional factors showed comparable predictive power	Liver disease: AUROC = 0.834 (microbiome + conventional) vs 0.768 (conventional); alcoholic liver disease: AUROC = 0.956 (microbiome + conventional) vs 0.875 (conventional)	Liu et al[31]
XGBoost (pre/delta-radiomics) + SMOTE	Pre/post-treatment MRI radiomics (n = 105); multisequence MRI integration	LARC patients n = 84; validation: 5/10-fold CV + independent; no control	Delta-radiomics > pre-radiomics	Pre-model: AUC 0.93 ± 0.06 (train)/0.79 (test); delta-model: AUC 0.96 ± 0.03 (train)/0.83 (test)	Wang et al[32]
sPLS-DA	Multi-site microbiome (saliva/esophagus/stomach); 16S rRNA analysis	EoE: Saliva = 29, biopsy = 25; controls: Non-EoE = 20 (saliva)/5 (biopsy)	Saliva model distinguishes EoE/non-EoE; esophageal microbiota detects disease activity	Saliva: CE 24%, validation Acc 78.6% (sensitivity 80%/specificity 75%); esophagus: CE 8% (activity detection)	Facchin et al[33]
sPLS-DA + LR	Genome-wide 5hmC features (n = 64); protein biomarkers	Healthy = 165; LC = 62; HCC = 135; longitudinal cohort	HCC diagnosis/recurrence prediction; tumor burden monitoring	Wild-score AUC = 93.24% (HCC vs non-HCC); HCC score AUC = 92.75% (HCC vs LC)	Cai et al[34]
LR + mixed-effects model	Multicohort clinical/serologic/genetic data; JAK-STAT/IL6 pathway	IBD patients = 12083 (4 cohorts); within-case design	Female/CD colonic location/surgery linked to EIMs; MHC/CPEB4 associations; therapeutic targets (TNF/JAK-STAT)	MHC OR = 2.5 (P = 1.4E-15); CPEB4 OR = 1.5 (P = 2.7 × 10-8); serologic panel OR = 1.7 (P = 3.6× 10-19)	Khrom et al[35]
LR + RF + kNN + SVM + NN	Recursive feature elimination; single-center retrospective	Total n = 864 (IIIa + n = 457 vs low-risk n = 407); 3-fold imputation/CV	NN outperforms others (Acc 68.8%); best in medical complications (AUC = 0.695)	NN: Overall Acc 0.688/AUC = 0.672; medical AUC = 0.695; surgical AUC = 0.653; cologne score Acc 0.510	Jung et al[36]
RF vs cv-Enet/glmboost/ensemble	Multicenter preoperative features; elastic-net regularization	Development = 3182 (39 centers); validation = 260; no control	RF optimal prediction; surgical decision support	RF AUC = 0.844 (0.841-0.848) (development); similar in validation	Pera et al[37]
LR + Cox regression models	Endoscopic features (whitish/irregular) + Histology (marked IM); retrospective multicenter	Total n = 182 (malignant = 48); progression cohort = 98; ROC/KM validation	Misdiagnosis predictors (single/large/IM); progression predictors (whitish/margin/multi-diagnosis)	AUC 0.871 (sensitivity 68.7%/specificity 92.5%)	Zou et al[38]
RF + Swin transformer tongue model	Questionnaire features (n = 10) + tongue images; multicenter	Total n = 2229 (9 centers); validation AUC > 0.8	Key factors: Age/TCM constitution/tongue features/diet/anxiety; dynamic nomogram	RF Acc 85.65%; tongue model Acc 73.33% (validation)	Yu et al[39]
LR	Tumor location/ulceration/biopsy features; H-L test/DCA validation	Training = 516; validation = 220 (7:3 split); no control	4 fibrosis predictors; severe fibrosis prediction model	Raining AUC = 0.819; validation AUC = 0.812; DCA clinical benefit	Zeng et al[40]
Stepwise logistic regression	Demographics/history/Lab markers (AFP/AST/albumin); prospective multicenter	Total n = 1723; HCC events = 109; median follow-up 2.2 years; no control	Key factors: Male/cirrhosis duration/family history/age/obesity/AFP/AST	Incidence 24/100 person-years; multivariate OR 1.08-2.73 (P < 0.05)	Reddy et al[41]
Multivariate logistic regression	Radiomic features (peritumoral enhancement/necrosis); transcriptomic sequencing	Development = 470; validation: Control = 145 + HAIC = 143; multicenter	Imaging subtypes guide HAIC benefit; immune pathway correlation	Training AUC = 0.83; control AUC = 0.84; HAIC AUC = 0.73	Ma et al[42]
LR	Multiphase CT radiomics (peritumoral); RNA sequencing	Total n = 773 (training 334 + internal 142 + external 141 + survival 121 + RNA35); 4 centers	MVI prediction + survival stratification (early recurrence/OS); glucose metabolism genes	Hybrid model AUC: 0.86 (internal)/0.84 (external); survival P < 0.01	Xia et al[43]
Multivariate logistic regression	LI-RADS visualization score (A/B/C); obesity class II-III	Total n = 2053 (A = 1685, B = 262, C = 106); longitudinal = 1546; multicenter	Alcohol/MASLD cirrhosis + obesity linked to limited visualization; 19.6% worsened/53.1% improved	Baseline limited rate 18%; obesity OR = 2.1 (P < 0.001)	Schoenberger et al[44]
Regularized LR + GBM	RCT secondary analysis; mailed outreach; prior screening behavior	Total n = 1200 (training 960 + test 240); 3 screening rounds; no control	Surveillance adherence stratification; key variables: Prior screening/primary care contact	AUROC 0.66-0.77 (increasing); 41%-47% completion rate	Singal et al[45]
LASSO logistic regression	Pre/intraoperative variables; multicenter international	Total n = 2192 (train 70% + valid 30%); 12 centers	Dual prediction (PHLF/CCI > 40); online risk calculators	PHLF AUC = 0.80 (calib. slope = 0.95); CCI AUC = 0.76	Wang et al[46]
LDpred2 PRS + QCancer-10 integration	Genetic/non-genetic factors; Cox proportional hazards	United Kingdom Biobank n = 434587; case-control/survival validation	C-index improvement (M + 7.3%/F + 6.5%); high-risk group 3.47 × (M)/2.77 × (F)	Integrated C-index: 0.730 (M)/0.687 (F); sensitivity/specificity: 47.8%/80.3% (M), 42.7%/80.1% (F)	Briggs et al[47]
Multivariable logistic + Cox regression	Multicenter FS screening; long-term follow-up (median 17 years)	Intervention = 40085 (13 centers)	High-ADR group: Distal CRC HR = 0.34 (incidence)/0.22 (mortality); all-site CRC HR = 0.58/0.52	High vs low-ADR: Distal CRC HR 0.34 vs 0.55 (incidence), 0.22 vs 0.54 (mortality)	Cross et al[48]
RRR + elastic net models	Inflammatory markers (CRP/IL6/GDF15) + metabolic markers (BMI/waist/C-peptide); case-control	Total n = 1368 (cases 684 +controls 684); NHS = 818F + HPFS = 550M	Sex-specific: Median OR = 1.34 (inflammation)/1.25 (metabolic); NS in F; 11 key metabolites	Variance explained: 24% (inflammation)/27% (metabolic)	Bever et al[49]
RSF/GBM/Deep hit	Multivariable analysis + clinical feature selection; time-dependent C-index	CRC patients = 2157; stratified 5-fold CV (5 repeats)	Deep hit best discrimination; RSF best calibration; SHAP key factors (R0 resection/TNM)	Deep hit C-index 0.789 (0.779-0.799); RSF brier 0.096 (0.094-0.099)	Yang et al[50]
Multivariable logistic regression	Cell search CTCs detection; prospective CTCs + retrospective HGP; excluded neoadjuvant/extrahepatic	Total n = 177 (dHGP = 34, 19%); multivariable validation; no external cohort	CTC-negativity predicts dHGP (OR = 2.7); dHGP better survival	OR = 2.7 (1.1-6.8), P = 0.028	Meyer et al[51]

SVM: Support vector machine; TCGA: The Cancer Genome Atlas; OS: Overall survival; DFS: Disease-free survival; AUC: Area under the curve; APINet: Attentive pairwise interaction neural network; TransFG: Transformer architecture for fine-grained recognition; GC: Gastric cancer; NGC: Non-gastric cancers; AG: Atrophic gastritis; LR: Logistic regression; kNN: K-nearest neighbors; PBMC: Peripheral blood mononuclear cells; RNA-seq: Ribonucleic acid sequencing; AH: Alcohol-associated hepatitis; AC: Alcohol-associated cirrhosis; MASLD: Metabolic dysfunction-associated steatotic liver disease; HCV: Hepatitis C virus; CT: Computed tomography; SRCC: Signet ring cell carcinoma; CI: Confidence interval; RF: Random forest; OPT: Optimal policy trees; MSKCC: Memorial Sloan Kettering Cancer Center; GIST: Gastrointestinal stromal tumours; RCT: Randomized controlled trials; DS: Duodenal stenting; EUS-GE: Endoscopic ultrasound-guided gastroenterostomy; GJ: Gastrojejunostomy; HE: Hematoxylin and eosin; WSI: Whole slide image; LASSO: Least absolute shrinkage and selection operator; HR: Hazard ratio; MSK: Memorial Sloan Kettering; GBM: Gradient boosting machine; OLGIM: Operative link on gastritis-intestinal metaplasia assessment; OLGA: Operative link on gastritis assessment; AUROC: The area under the receiver operating characteristic curve; XGBoost: eXtreme gradient boosting; SMOTE: Synthetic minority oversampling technique; sPLS-DA: Sparse partial least squares-discriminant analysis; MRI: Magnetic resonance imaging; CV: Cross validation; EoE: Eosinophilic oesophagitis; Acc: Accuracy; CE: Classification error; NN: Neural network; JAK: Janus kinase; STAT: Signal transducers and activators of transcription; IL-6: Interleukin-6; LC: Liver cirrhosis; HCC: Hepatocellular carcinoma; IBD: Inflammatory bowel disease; CD: Crohn’s disease; EIM: Extraintestinal manifestations; MHC: Major histocompatibility complex; CPEB4: Cytoplasmic polyadenylation element binding protein 4; TNF: Tumor necrosis factor; OR: Odds ratio; IM: Intestinal metaplasia; ROC: Receiver operating characteristic curve; KM: Kaplan-Meier; TCM: Traditional Chinese medicine; H-L: Hosmer-Lemeshow; DCA: Decision curve analysis; AFP: Alpha-fetoprotein; AST: Aspartate aminotransferase; HAIC: Hepatic arterial infusion chemotherapy; MVI: Microvascular invasion; LI-RADS: Liver imaging, reporting and data system; PHLF: Posthepatectomy liver failure; CCI: Comprehensive complication index; M: Male; F: Female; ADR: Adenoma detection rate; CRC: Colorectal cancer; FS: Flexible sigmoidoscopy; CRP: C-reactive protein; GDF15: Growth differentiation factor 15; BMI: Body mass index; NS: Neither signature; RSF: Random survival forest; SHAP: SHapley Additive exPlanations; TNM: Tumor-node-metastasis; CTCs: Circulating tumour cells; HGP: Histopathological growth pattern; dHGP: Desmoplastic histopathological growth pattern.