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

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.