Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis

Table 1 Summary of artificial intelligence in esophageal diseases

Disease	Application	Ref.	Study design	Region/country	Modality	Test set	AI model	Main findings
BE	Diagnosis	Rosenfeld et al[19]	R	United Kingdom	Questionnaires	1299 patients	ML	ML model with 8 factors (e.g., age) predicts BE (AUC 0.86/0.81), facilitating high-risk screening
	Diagnosis	Abdelrahim et al[20]	P	Europe	WLI	75 patients	CNN	An AI system detected Barrett's neoplasia in real-time endoscopy with 93.8% sensitivity, significantly higher than endoscopists (63.5%)
	Diagnosis	Struyvenberg et al[21]	R	Europe	NBI	157 videos	CNN	Developed a DL-based CAD system for Barrett's neoplasia in NBI videos: 83% accuracy, 85% sensitivity, 83% specificity, processing at 38 fps
	Diagnosis	Hashimoto et al[22]	R	United States	WLI, NBI	1832 images	CNN	AI detects Barrett's early neoplasia at 95.4% accuracy, 96.4% sensitivity via CNN, with real-time lesion localization
Esophageal carcinoma, ESCC	Diagnosis	Tokai et al[23]	R	Japan	WLI, NBI	2042 images	CNN	AI outperformed 13 endoscopists in assessing ESCC invasion depth (accuracy: 80.9%; AUC 0.7873), demonstrating superior diagnostic capability
	Diagnosis	Fukuda et al[24]	R	Japan	NBI, BLI	28333 images	CNN	AI outperformed endoscopists in ESCC detection sensitivity (91% vs 79%) and characterization accuracy (88% vs 75%)
	Diagnosis	Li et al[25]	R	China	WLI, NBI	759 patients	DL	CAD-NBI surpasses CAD-WLI in accuracy/specificity for early ESCC (P < 0.05). Endoscopist combination yields optimal diagnosis (94.9% accuracy, 92.4% sensitivity, 96.7% specificity)
	Diagnosis	Guo et al[26]	R	China	NBI	13144 images	DL	This DL model demonstrates high sensitivity (image 98.04%, video 96.1%) and specificity (image 95.03%, video 99.9%) in real-time diagnosis of esophageal precancerous and early SCC
	Diagnosis	Ohmori et al[27]	R	Japan	WLI, NBI/BLI, ME	21597 images	CNN	AI detected ESCC via non-magnifying endoscopy (NBI/BLI) with 100% sensitivity. With magnification, accuracy reached 83%, comparable to expert endoscopists

R: Retrospective; P: Prospective; ML: Machine learning; CNN: Convolutional neural network; DL: Deep learning; WLI: White light imaging; NBI: Narrow band imaging; BLI: Blue laser imaging; ME: Magnifying endoscopy; AUC: Area under curve; CAD: Computer aided design; ESCC: Esophageal squamous cell carcinoma; SCC: Squamous cell carcinoma; AI: Artificial intelligence; BE: Barrett's esophagus.

Table 2 Summary of artificial intelligence in gastric diseases

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
H. pylori infection	Diagnosis	Martin et al[43]	R	United States	Gastric biopsy	406 patients	CNN	DCNNs accurately recognize gastric pathology damage patterns, especially H. pylori gastritis, and serve as effective screening tools
	Diagnosis	Mohan et al[44]	R	China, Japan	WLI, BLI, LCI	-	CNN	For H. pylori infection diagnosis, CNN achieved 87% accuracy, sensitivity, and specificity, comparable to endoscopists (82.9% accuracy)
	Diagnosis	Nakashima et al[45]	R	Japan	LCI, WLI	515 patients	CNN	Developed LCI/DL-based CAD classifying H. pylori infection into uninfected, active, and post-eradication statuses with 84.2%, 82.5%, 79.2% accuracy. Outperforms WLI and matches expert endoscopists
Gastric polyp	Diagnosis	Yuan et al[46]	R	China	WLI	9443 patients	DCNNs	AI achieved 96.2% accuracy and 88.0% sensitivity for gastric polyps. With AI, junior endoscopists' accuracy significantly improved (96.9%→97.6%), matching seniors
	Diagnosis	Cao et al[47]	R	China	Gastroscopic imaging	2270 images	DL	Improved YOLOv3 with feature fusion boosts small polyp detection in gastroscopic images to 91.6% accuracy, resolving complex background interference
GC	Diagnosis	Horiuchi et al[48]	R	Japan	ME-NBI	2828 images	CNN	CNN system distinguishes EGC from gastritis (sensitivity 95.4%, NPV 91.7%, accuracy 85.3%), aiding clinical diagnosis
	Diagnosis	Li et al[39]	P & R	China	ME-NBI	20341 images	CNN	ME-NBI-based CNN achieves 90.91% accuracy for early GC; 91.18% sensitivity (superior to experts), 90.64% specificity (comparable); overall outperforms non-experts
	Diagnosis	Bu et al[49]	P	China	Liquid biopsy	150 samples	ML	Developing NanoFisher for efficient plasma EV isolation, combining metabolomics and machine learning, achieves 92% accuracy in EGC diagnosis
	Treatment	Wang et al[50]	R	China	CECT	244 patients	ML	CT radiomics distinguishes T2 from T3/T4 GC, guiding neoadjuvant chemotherapy selection
	Treatment	Shang et al[51]	R	China	CECT	311 images	DL	A nomogram built from radiomic and DL features via automated spleen segmentation effectively predicts GC serosal invasion, providing a noninvasive tool for surgical planning
	Treatment	Kang et al[52]	R	South Korea	CT, WLI, biopsy	2927 patients	DL	Developed a Transformer-based multimodal AI system integrating endoscopic images and clinical data. Accurately predicts EGC lymph node metastasis risk (AUC 0.908), guiding treatment decisions
	Treatment	Chen et al[53]	R	China	Laparoscopic surgery video	2460 images	DL	Develop AI models to accurately identify perigastric vessels, enhancing safety and reducing bleeding risks in laparoscopic gastrectomy
	Prognosis	Zhang et al[54]	R	China	CT	669 patients	DCNN	Developed CT-based radiomics nomogram integrating radiomics and clinical factors (e.g., CEA) effectively predicts early recurrence in advanced GC preoperatively (AUC 0.806-0.831)
	Prognosis	Dong et al[55]	R	China, Italy	CECT	730 patients	DL	DLRN accurately predicts GC lymph node metastasis number (C-index 0.797-0.822), outperforming clinical staging and correlating significantly with survival
	Prognosis	Huang et al[56]	R	China	CECT	205 patients	ML	Developed a ML nomogram combining clinical factors (T/N stage) and CT radiomics to predict gastric cancer PNI (validation AUC 0.885), aiding prognosis

R: Retrospective; P: Prospective; ML: Machine learning; LCI: Linked color imaging; CT: Computed tomography; CECT: Contrast-enhanced computed tomography; ME-NBI: Magnifying endoscopy with narrow band imaging; EGC: Early gastric cancer; GC: Gastric cancer; DCNN: Deep convolutional neural networks; H. pylori: Helicobacter pylori; CNN: Convolutional neural network; WLI: White light imaging; BLI: Blue laser imaging; CAD: Computer aided design; AI: Artificial intelligence; EV: Extracellular vesicle; AUC: Area under curve; CEA: Carcinoembryonic antigen; DLRN: Deep learning radiomic nomogram; PNI: Prognostic nutritional index; DL: Deep learning.

Table 3 Summary of artificial intelligence in intestinal diseases

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
Crohn's disease	Diagnosis	Li et al[66]	R	China	CTE, histopathology	167 patients	ML	A validated CTE-based radiomics model accurately distinguishes moderate-severe from non-mild fibrosis in Crohn's bowel walls, significantly outperforming radiologists' visual assessments
	Diagnosis	Majtner et al[67]	P	Denmark	pan-CE	7744 images	DL	Auto-detects Crohn's ulcers with 98.4% accuracy. Comparable small/Large bowel accuracy (98.5% vs 98.1%). Distinguishes severity (κ = 0.72)
	Diagnosis	Klang et al[68]	R	Israel	CE	27892 images	CNN	DL model detects Crohn's enteric strictures at 93.5% accuracy (AUC 0.989), precisely distinguishing strictures from ulcers (including severe), enabling automated CE diagnosis
	Treatment	Konikoff et al[69]	R	Israel	APCT	101 patients	ML	Developed an ML model using indicators, such as NLR, to predict Crohn's complication risk in emergency CT (AUC 0.774), enabling risk stratification to reduce unnecessary scans
	Treatment	Con et al[70]	R	Australia	Serum biomarkers	146 patients	DL	RNN with serial biomarkers (AUC 0.754) outperforms logistic regression (AUC 0.659) in predicting biochemical remission (CRP < 5 mg/L) at 12 months post-anti-TNF therapy in Crohn's disease
	Prognosis	Ungaro et al[71]	P	United States, Canada	PEA	265 patients	ML	ML model identified 5 plasma protein markers for penetrating (B3) and 4 for stricturing (B2) complications, outperforming traditional models (B3 AUC 0.79)
	Prognosis	Stidham et al[72]	R	United States	Laboratory examination	2809 patients	ML	Machine learning leveraging routine longitudinal lab data predicts Crohn's surgical risk (AUC 0.78)
SINENs	Diagnosis	Kjellman et al[73]	P	Northern Europe	PET/CT, MRI etc.	278 patients	ML	Multi-plasma protein markers with Random Forest boost SI-NETs diagnosis (Sensitivity 89%, Specificity 91%, AUC 0.99)
	Diagnosis	Clift et al[74]	R	United Kingdom	EHR	382 patients	ML	XGBoost using primary care EHRs effectively identifies undiagnosed high-risk SI-NET patients (AUC 0.869)

R: Retrospective; P: Prospective; CTE: Computed tomography enterography; ML: Machine learning; pan-CE: Pan-enteric capsule endoscopy; APCT: Abdominopelvic computed tomography; RNN: Recurrent neural network; PEA: Proximity extension assay; HER: Electronic health records; DL: Deep learning; AUC: Area under curve; CE: Capsule endoscopy; CT: Computed tomography; CRP: C-reactive protein; TNF: Tumor necrosis factor; PET: Positron emission tomography; MRI: Magnetic resonance imaging; SI-NETs: Small intestine neuroendocrine tumours; SINENs: Small intestinal neuroendocrine neoplasms; NLR: Neutrophil-to-lymphocyte ratio.

Table 4 Summary of artificial intelligence in colorectal diseases

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
Ulcerative colitis	Diagnosis	Sutton et al[99]	R	Norway	Endoscopy	8000 images	CNN	AI (especially DenseNet121 model) accurately distinguishes UC from non-UC pathology (AUC 0.999) and grades endoscopic activity (mild/severe, AUC 0.90)
	Diagnosis	Lo et al[100]	R	Denmark	WLI	1484 images	CNN	The CNN model achieved 84% accuracy in distinguishing UC endoscopic severity (Mayo score 0-3), significantly outperforming existing models and standardizing clinical assessment
	Diagnosis	Ruan et al[101]	R	China	Colonoscope	1772 patients	CNN	AI model detects UC/CD at 99.1% accuracy vs physicians' 78%-92.2%, enhancing clinical efficiency
	Diagnosis	Gutierrez Becker et al[102]	R	Europe et al	Endoscopy	1672 videos	CNN	This model directly analyzes raw colonoscopy videos, automatically assessing UC severity (MCES) with high accuracy (AUC 0.84-0.85), reducing manual annotation needs
	Treatment	Bossuyt et al[103]	P	Belgium, Japan	Endoscopy	100 patients	ML	The RD algorithm objectively evaluates UC endoscopic and histologic activity, closely correlated with RHI (r = 0.74), and is sensitive for monitoring therapeutic response
	Treatment	Iacucci et al[104]	P	Europe, North America	HD-WLE, VCE	283 patients	CNN	AI system accurately differentiates UC endoscopic activity/remission (AUC 0.94) and predicts histological remission (83% accuracy), comparable to physicians
	Prognosis	Huang et al[105]	R	China	Colonoscope	856 images	DL, ML	DL/ML-CAD diagnoses mucosal healing (MES 0-1) with 94.5% accuracy and complete healing (MES 0) at 89.0%
	Prognosis	Popa et al[106]	R	Romania	Colonoscope	55 patients	ML	ML models accurately predict endoscopic disease activity one year after anti-TNFα therapy in UC patients (90% accuracy in test set, 100% in validation set)
	Prognosis	Takenaka et al[107]	P	Japan	Endoscopy	2012 patients	DNN	DNN achieves 90.1% accuracy for endoscopic remission and 92.9% for histological remission (UC), reducing biopsy needs
	Prognosis	Maeda et al[108]	P	Japan	Endocytoscope, NBI	145 patients	ML	Real-time AI endoscopy predicts relapse risk in UC remission by analyzing mucosal microvessels (AI-Active 28.4% vs AI-Healing 4.9%, P < 0.001)
Colorectal polyps	Diagnosis	Wang et al[109]	R	China	Colonoscope	1600 patients	CNN	Enhanced GAP model achieves > 98% accuracy (TPR > 96%, TNR > 98%) for colon polyp detection with reduced parameters, enabling lightweight yet accurate diagnosis
	Diagnosis	Song et al[89]	P & R	South Korea	NBI	1169 samples	DL	CAD with NBI predicts polyp histology at 81.3%-82.4% accuracy, outperforming junior physicians (63.8-71.8%) and matching experts (82.4-87.3%), enhancing junior diagnostic performance
	Diagnosis	Jin et al[110]	P & R	South Korea	NBI	2450 images	CNN	AI assistance significantly boosts endoscopists' (especially novices') accuracy for small polyps (< 5 mm) (73.8%→85.6%) and reduces time (3.92→3.37 seconds/polyp)
	Diagnosis	Sakamoto et al[111]	R	Japan	WLI, LCI, BLI	1788 images	DL	CADe sensitive > 94% (WLI/LCI), CADx accuracy > 93% (WLI/BLI), rivals expert endoscopists
	Diagnosis	Zachariah et al[112]	R	USA	WLI, NBI	6223 images	CNN	CNN real-time prediction of colorectal polyp pathology meets PIVI standards: 97% adenoma NPV, > 93% surveillance interval concordance
	Treatment	Wickstrøm et al[113]	R	Europe	Colonoscope	912 images	FCNs	CNNs rely on polyp shape/edges for segmentation; error risk rises significantly in uncertain areas. FCNs combine uncertainty with interpretability visualization, helping doctors pinpoint high-risk regions fast
	Treatment	Su et al[114]	P	China	Colonoscope	659 patients	DCNN	AQCS significantly boosts adenoma detection (28.9% vs 16.5%, P < 0.001), polyp detection, and optimizes withdrawal time and bowel prep during colonoscopy
CRC	Diagnosis	Luo et al[115]	P & R	China	Liquid biopsy	3315 patients	ML	Plasma ctDNA methylation markers (e.g., cg10673833) enable early CRC diagnosis (AUC 0.96) and high-risk group screening (Sensitivity 89.7%)
	Diagnosis	Arabameri et al[116]	R	France, USA, Austria	Fecal microbiota analysis	350 patients	ML	Combining GRNN and DBFS (new feature selection) identified 6 key microbial markers (e.g., Clostridium), enabling high-precision CRC detection (AUC 0.911) but insufficient adenoma sensitivity (AUC 0.724)
	Diagnosis	Zeng et al[117]	P	USA	OCT	26000 images	CNN	PR-OCT system using OCT and RetinaNet distinguishes CRC from normal tissue in real-time with high accuracy (sensitivity 100%, specificity 99.7%, AUC 0.998)
	Treatment	Wang et al[118]	R	China	MRI	240 patients	Faster R-CNN	Faster R-CNN detects positive CRM on rectal cancer pre-op high-resolution MRI with 93.2% accuracy (AUC 0.953); 0.2 seconds/image, highly feasible and efficient
	Treatment	Yang et al[119]	R	China	MRI	89 patients	ML	Pre-treatment ADC radiomics predicts locally advanced rectal cancer resistance to neoadjuvant chemoradiotherapy (AUC 0.83/91.3% accuracy)
	Treatment	Fu et al[120]	R	USA	MRI	43 patients	DL	DL-based radiomics significantly outperform handcrafted features in predicting neoadjuvant chemoradiotherapy response for locally advanced rectal cancer (AUC 0.73 vs 0.64)
	Prognosis	Xu et al[121]	R	China	CT, MRI et al	999 patients	ML	GradientBoosting and LightGBM effectively predict stage IV CRC recurrence risk (AUC up to 0.881); key factors: Chemotherapy, age, LogCEA, CEA, anesthesia duration
	Prognosis	Zhao et al[122]	R	China	Clinical data	7205 patients	ML	ML-based NCDB nomogram predicts metastatic rectal cancer 3-year OS (C-index > 0.77, internal/external validation), outperforming prior models
	Prognosis	Reichling et al[123]	R	France	IHC, WSI	1018 patients	ML	DGMuneS integrating tumor-stroma/CD3+/tumor features better predicts stage III colon cancer recurrence than traditional immune scores (C-index 0.601 vs 0.578)
	Prognosis	Skrede et al[124]	R	Norway, United Kingdom	H&E staining	2467 patients	CNN	Developed a DL-based prognostic biomarker (DoMore v1-CRC) using only routine H&E-stained slides, effectively stratifying Stage II/III CRC risk and outperforming existing markers
	Prognosis	Väyrynen et al[125]	P	USA	H&E staining	1504 samples	ML	ML on H&E slides links dense stromal lymphocytes/eosinophils and their peri-tumoral localization to significantly improved CRC-specific survival

R: Retrospective; P: Prospective; RHI: Robarts Histological Index; RD: Red density; HD-WLE: High definition white light endoscopy; VCE: Virtual chromoendoscopy; TPR: True positive rate; TNR: True Negative Rate; PIVI: Preservation and incorporation of valuable endoscopic innovations; NPV: Negative predictive value; FCNs: Fully convolution networks; AQCS: Automatic quality control system; GRNN: Generalized regression neural network; DBFS: Derivative-based feature selection; OCT: Optical coherence tomography; R-CNN: Faster R-convolutional neural network; CRM: Circumferential resection margin; ADC: Apparent diffusion coefficient; OS: Overall survival; WSI: Whole slide imaging; IHC: Immunohistochemistry; H&E: Hematoxylin & eosin; CRC: Colorectal cancer.

Table 5 Summary of artificial intelligence in hepatic disease

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
Hepato-cirrhosis	Diagnosis	Rhyou and Yoo[146]	R	South Korea	US	4950 images	DL	A patch-based DL network for ultrasound cirrhosis diagnosis using synthetic image augmentation, achieving 99.95% accuracy, 100% sensitivity, and 99.9% specificity
	Diagnosis	Luetkens et al[147]	R	Germany	MRI	465 patients	CNN	ResNet50 distinguishes alcoholic from nonalcoholic cirrhosis on MRI: AUC 0.82, 75% accuracy
	Diagnosis	Chang et al[148]	R	United States	Liver biopsy, FibroScan etc.	1370 patients	ML	ML models (especially Random Forest) outperform traditional non-invasive tests (e.g., FibroScan, FIB-4) in identifying significant fibrosis and cirrhosis in NAFLD patients
	Diagnosis	Mazumder et al[149]	R	United States	CT, liver biopsy etc.	351 patients	DL, ML	Combining AI-extracted CT radiomics with routine lab data boosts liver cirrhosis prediction accuracy (AUC 0.84-0.85)
	Diagnosis	Guo et al[150]	P	United Kingdom	NMR spectroscopy	64005 patients	ML	A plasma metabolomics and ML-based nomogram accurately predicts 10-year hepatic cirrhosis complication risk (AUC 0.861), outperforming conventional metrics
Hepatic encephalopathy, HE	Diagnosis	Calvo Córdoba et al[151]	P	Spain	VOG	47 patients	SVM	Automated VOG with SVM detects MHE in 7-10 minutes (93% sensitivity/specificity), outperforming PHES (25-40 minutes)
	Diagnosis	Sparacia et al[152]	R	Italy	MRI	124 patients	ML	MRI radiomics with KNN predicts HE presence (76.5% accuracy); MLP predicts HE severity (≥ stage 2, 94.1%), demonstrating potential for HE diagnosis and staging
	Diagnosis	Chen et al[153]	R	China	MRI	53 patients	SVM	SVM model using gray matter volume discriminates cirrhosis patients with/without MHE at 83.02% accuracy
	Treatment	Liu et al[154]	R	China	TIPS	218 patients	ML	Developed logistic regression model (AUC 0.825) accurately predicts OHE post-TIPS
	Treatment	Zhong et al[155]	R	China	TIPS	207 patients	ANN	ANN model accurately predicts post-TIPS OHE (C-index = 0.863), with 15.9% incidence within 3 months, providing a clinical stratification tool
Liver cancer	Diagnosis	Gao et al[156]	R	China	CECT	723 patients	DL	This model effectively distinguishes malignant liver tumors (HCC, ICC, metastases), achieving 72.6% test-set accuracy and improving physician ICC sensitivity by 26.9%
	Diagnosis	Xu et al[157]	R	China	CT	1049 patients	DL	Swin-Transformer model simplifies LI-RADS classification, effectively distinguishes HCC from non-HCC, and enhances diagnostic performance with clinical data
	Diagnosis	Li et al[158]	R	China	DECT	262 patients	DL	Dual-energy CT deep-learning radiomics nomogram noninvasively predicts HCC MTM subtype, outperforming clinical-radiologic models (AUC 0.87-0.91)
	Diagnosis	Ma et al[159]	R	China	CT, MRI	211 patients	ML	CT/MRI radiomics + clinical features (SVM) achieves highest HCC diagnostic accuracy (82.4%), significantly outperforms single-modality models, and distinguishes HCC vs non-HCC
	Treatment	Hua et al[144]	R	China	CECT	151 patients	ML	Developed and validated a pretreatment CT-based radiomics model predicting response and survival outcomes after triple therapy in unresectable HCC to guide clinical decisions
	Treatment	Xu et al[143]	R	China	CECT	458 patients	DL	The model significantly outperforms single models (externally validated AUC 0.896), effectively distinguishing survival differences (P < 0.001), providing a tool for personalized treatment
	Treatment	An et al[160]	R	China	IATs	2959 patients	ML	Developed MLDSM to risk-stratify unresectable HCC patients undergoing transarterial therapies (alone/combined) for 12-month mortality, guiding clinical treatment decision (e.g. TACE, HAIC)
	Prognosis	Cao et al[161]	R	China	CT, MRI et al	466 patients	DL, ML	Developed pre-/post-op dual-phase DeepSurv model predicting HCC recurrence post-liver transplant; outperformed Milan Criteria (C-index 0.765-0.839), guiding individualized surveillance
	Prognosis	Altaf et al[162]	R	Pakistan	CT, MRI etc.	192 patients	CNN	AI model with tumor size, AFP, and grade predicts post-transplant HCC recurrence risk (validation AUC 0.77)
	Prognosis	Dong et al[163]	R	United States	Clinical data	2038 patients	ML	XGBoost model effectively predicts 1-, 3-, and 5-year survival (AUC > 0.7) in AFP-positive HCC patients, outperforming other algorithms, offering a clinical tool for early intervention
	Prognosis	Yan et al[164]	R	China	MRI	285 patients	CNN	Deep learning-based nomogram integrating imaging, MVI, and tumor number significantly outperforms traditional models in predicting early HCC recurrence (AUC 0.949 vs 0.751)

R: Retrospective; P: Prospective; US: Ultrasound; NMR: Nuclear magnetic resonance; VOG: Video-oculographic; SVM: Support vector machine; PHES: Psychometric hepatic encephalopathy score; MLP: Multilayer perceptron; KNN: K-nearest neighbor; TIPS: Transjugular intrahepatic portosystemic shunt; OHE: Overt hepatic encephalopathy; ANN: Artificial neural network; CT: Computed tomography; CECT: Contrast-enhanced computed tomography; ICC: Intrahepatic cholangiocarcinoma; DECT: Duel-energy computed tomography; IATs: Intra-arterial therapies; TACE: Transarterial chemoembolization; HAIC: Hepatic arterial infusion chemotherapy; MVI: Microvascular invasion; HE: Hepatic encephalopathy; HCC: Hepatocellular carcinoma.