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.

Table 2 Emerging deep learning approaches in gastrointestinal disease management (2022-2025)[52-77]

AI algorithm	Parameters employed/study design	Sample size, control group, validation	Outcomes	Performance	Ref.
CNN	14 EUS anatomical sites; multicenter validation	Training: 1812 patients/6230 images; internal: 47 patients/1569 images; external: 131 patients/85322 images	Outperformed novices in 11 sites; high expert agreement (kappa 084-0.98)	Internal Acc 92.1-100%; external sensitivity 89.45%-99.92%/specificity 93.35%-99.79%	Tian et al[52]
NNLS deconvolution + GCNN	Methylation atlas (TSMA) + genome-wide density; multi-modal strategy	5 tumor types + WBC training; validation = 239 low-depth cfDNA	Multi-modal improves TOO in low-depth cfDNA	Validation Acc 69%	Nguyen et al[53]
CNN + survival MLP	CT + clinical multimodal data; 5-fold CV	GC patients = 1061; vs 3 SOTA methods; no control	Multimodal > single-modality; optimal OS/PFS prediction	OS C-index 0.849; PFS 0.783 (surpass SOTA)	Hao et al[54]
CNN	HE features for HER2 status; trastuzumab response	Surgical = 300; biopsy = 101; treated = 41; no control	HER2 amplification prediction; treatment response (CR + PR vs SD + PD)	Surgical AUC 0.847 (amplification)/0.903 (2 +); biopsy 0.723; treatment 0.833	Wu et al[55]
DCNN	HE whole-slide imaging; fibrosis stage comparison	Non-HCC = 639; HCC = 46; paired training/unpaired validation	Detect HCC risk in mild fibrosis; saliency maps reveal nuclear atypia/immune infiltration	Training Acc 81.0% (AUC = 0.80); validation 82.3% (AUC = 0.84)	Nakatsuka et al[56]
Faster R-CNN model	Preoperative CT/MRI analysis; multicenter retrospective cohort (2012-2020)	Total n = 1141 (PCCCL = 62, CHCC = 1079); 4:1 split (train-val vs test); CHCC cases (n = 1079) as negative control	Differential diagnosis of rare PCCCL	Accuracy: 0.962 (95%CI: 0.931-0.992); AP: PCCCL 0.908, CHCC 0.907; Recall: 0.95	Liu et al[57]
Transformer	End-to-end biomarker prediction; multicenter validation	Total n > 13k (16 CRC cohorts); resection training/biopsy validation	Solved biopsy MSI diagnosis; improved interpretability	MSI detection: Sensitivity 0.99/NPV > 0.99	Wagner et al[58]
CNN + SMOTE/SVM	Pathomics/radiomics/immune score (CD3 +/CD8 +)/clinical; digital pathology	Lung metastasis = 103; internal validation	Path/radio features vs immunoscore (neg); triple independent prognosis	Integrated model: OS = 0.860/DFS = 0.875; Calib/DCA validated	Wang et al[59]
INSIGHT (CNN) + wise MSI (self-attention) two-stage	Tumor tile classification + ResNet pre-trained + attention pooling; multicenter	Chinese multicenter cohort; vs 5 DL methods	Outperforms SOTA in MSI prediction; high pathologist consistency	Wise MSI AUC 0.954 (0.948-0.960)	Chang et al[60]
CNN + RNN	Multicenter blinded trial; real-time monitoring + second observer	Total n = 946 (adenomas = 989); multicenter	CADe > human in adenoma detection (sensitivity 94.6% vs 96.0%); changed 2.3% follow-up	ADR + 1.1%/case; Non-neoplastic + 4.9%; time + 42.6% (6.6 minutes)	Sinonquel et al[61]
ANN	Pathological image analysis; retrospective multicenter	Training = 496 (GDPH); external validation = 150 (SYSMH)	Avoided 34.9% unnecessary surgeries; outperformed United States guidelines	Training AUC = 0.979; validation AUC = 0.978	Su et al[62]
Multitask transformer	Preop MRI multiparametric features; 7-center retrospective	Total n = 725 (train 234 + internal 58); external = 212/111/110	PA-TACE benefit in high-MVI/low-survival group (P < 0.001)	RFS C-index: Training 0.763/validation 0.628-0.728	Wang et al[63]
Multistage DL models	Longitudinal MRI (pre/post-TA) + clinical variables; multicenter retrospective	Total n = 289 (train 254 + external 35); 3 hospitals	DL clinical improved ER prediction (AUC = 0.740); High/low-risk RFS P = 0.04	DL clinical AUC: 0.740 vs 0.571/0.648/0.689	Kong and Li[64]
CNN	Clinical data + MRI radiomics; 6 time-frame prediction	Early HCC = 120 (recurrence = 44); retrospective (2005-2018)	Imaging model > clinical (AUC 0.76 vs 0.68, P = 0.03)	Imaging model AUC 0.71-0.85; KM P < 0.05 (2-6 years)	Iseke et all[65]
RSF/ANN/decision tree	Inflammatory markers + ALBI + AFP + tumor size + INR; single-center retrospective	Total n = 808 (train 2:1 split)	ANN optimal (5 years AUC = 0.85); High-risk OS HR = 7.98 (5.85-10.93)	Training AUC 0.85 (0.82-0.88); validation 0.82 (0.74-0.85); P < 0.0001	Zhang et al[66]
DL	DCE-MRI + clinical/radiologic features; retrospective multicenter	Total n = 355 (train 251 + internal 62 + external 42); 2 centers	Proliferative HCC prediction; fusion model improves recurrence stratification	DL + clinical + radiologic model AUC: Training 0.99/internal 0.87/external 0.80	Qu et al[67]
DenseNet169 + MLP	Multiphase 25D CT + clinical features + RNA-seq; multicenter retrospective	Total n = 620 (TCIA + 3 centers); internal + 2 external test sets	Stratified RFS/OS (P < 0.001); high score links WNT/MYC/KRAS activation	DLER MLP 0.891 vs DLER 0.797 vs clinical model 0.752	Guo et al[68]
scSE-CatBoost	Multi-site endoscopic images; CNN + scSE feature extraction	Total n = 302 (An Nan Hospital); RUT validation	Real-time Helicobacter pylori detection; NPV 100%	Acc 0.90; sensitivity 1.00/specificity 0.81; AUC = 0.88	Lin et al[69]
Transformer + MIL	HE WSIs; dual-task (subtype + TMB prediction)	EC = 529/918; CRC = 594/1495; vs 7 SOTA methods	Strong subtype-TMB association (fisher P < 0.001); guides immunotherapy	Outperformed SOTA in both tasks	Wang et al[70]
GAN + ViT distillation	HE/HPS staining; multi-task prognosis (OS/TTR/TRG)	Internal = 258 CLM; two public datasets	TRG dichotomization. Acc 86.9-90.3%; 3-class Acc 78.5-82.1%	OS C-index 0.804 (± 0.014); TTR C-index 0.735 (± 0.016)	Elforaici et al[71]
Transfer learning	HE WSIs analysis	Segmentation = 100 WSI; validation: 4 cohorts (3 internal +1 external) + 6-month series = 217	Fine-tuning improved F1 0.797-0.949 (P < 0.00001); 100% visual overlay accuracy	Detection model AUC 0.959-0.978 (P < 0.00001)	Khan et al[72]
DBMIA-Net	GIA + EIA modules; adaptive channel graph convolution	5 public datasets (CVC-Clinic DB); vs SOTA methods	Enhanced generalization	94.12% dice (vs PraNet + 4.22%); leading in 6 metrics	Zhang et al[73]
UC-former vision transformer	Multicenter retrospective study; mayo endoscopic score prediction	Total n = 768 UC patients/15120 images; internal + 3 external validations	Surpassed senior endoscopists; strong multicenter stability	Internal Acc 90.8%; external Acc 82.4%-85.0%	Qi et al[74]
MIST	Self-supervised contrastive learning + dual-stream MIL	Total n = 480/666 WSI (Drum Tower); external = 273 WSI (Nanjing First)	Acc comparable to pathologists (0.784 vs 0.806)	External Acc 0.784	Cai et al[75]
ResTransUNet	Global context (transformer) + local features (CNN); LiTS2017/3Dircadb/Chaos/Sliver07	LiTS2017/3Dircadb/Chaos/Sliver07	Solved small/discontinuous region segmentation; outperformed SOTA	LiTS2017 dice 09535/VOE 0.0804/RVD -0.0007	Ou et al[76]
GCN	Pathological micronecrosis analysis + multicenter datasets; GCN feature fusion	Total n = 752/3622 slides; internal (FAH-ZJUMS) + external (TCGA-LIHC)	Improved prognostic stratification; precise necrosis localization	Internal + 8.18%; External + 9.02%; superior C-index vs baseline	Deng et al[77]

CNN: Convolutional neural network; EUS: Endoscopic ultrasonography; Acc: Accuracy; NNLS: Non-negative least square matrix factorization; WBC: White blood cells; TSMA: Tumor-specific methylation atlas; GCNN: Graph convolutional neural network; TOO: Tissue-of-origin; cfDNA: Cell free DNA; MLP: Multilayer perceptron; CT: Computed tomography; CV: Cross validation; GC: Gastric cancer; SOTA: State-of-the-art methods; OS: Overall survival; PFS: Progression-free survival; HE: Hematoxylin and eosin; HER2: Human epidermal growth factor receptor 2; CR: Complete response; PR: Partial response; SD: Stable disease; PD: Progressive disease; AUC: Area under the curve; DCNN: Deep convolutional neural networks; HCC: Hepatocellular carcinoma; MRI: Magnetic resonance imaging; PCCCL: Primary clear cell carcinoma of the liver; CHCC: Common hepatocellular carcinoma; CRC: Colorectal cancer; MSI: Microsatellite instability; NPV: Negative predictive value; SMOTE: Synthetic minority oversampling technique; SVM: Support vector machine; CD: Cluster of differentiation; DCA: Decision curve analysis; DL: Deep learning; RNN: Recurrent neural network; ANN: Artificial neural network; CADe: Computer-aided detection; ADR: Adenoma detection rates; GDPH: Guangdong Provincial People’s Hospital; SYSMH: Sun Yat-Sen Memorial Hospital; MVI: Microvascular invasion; PA-TACE: Postoperative adjuvant transcatheter arterial chemoembolization; RFS: Recurrence-free survival; TA: Thermal ablation; ER: Early recurrence; KM: Kaplan-Meier; HR: Hazard ratio; ALBI: Albumin-bilirubin; AFP: Alpha-fetoprotein; INR: International normalized ratio; DCE-MRI: Dynamic contrast enhanced-magnetic resonance imaging; RNA-seq: Ribonucleic acid sequencing; TCIA: The cancer image archive; DLER: Deep learning model for early recurrence prediction; scSE: Spatial and channel squeeze and excitation block; RUT: Rapid urease test; TMB: Tumor mutational burden; MIL: Multiple instance learning model; GAN: Generative adversarial network; ViT: Vision transformers; HPS: Hematoxylin phloxine saffron; TRG: Tumor regression grade; TTR: Time-to-recurrence; WSI: Whole-slide image; GIA: Global information aggregation; EIA: Edge information aggregation; CVC-clinic DB: Colorectal cancer-clinic dataset; UC: Ulcerative colitis; MIST: Multiple instance learning network based on swin transformer; VOE: Volume overlap error; RVD: Relative volume difference; GCN: Graph convolutional neural networks; FAH-ZJUMS: The First Affiliated Hospital of Zhejiang University School of Medicine Datasets; TCGA-LIHC: The Cancer Genome Atlas liver hepatocellular carcinoma.