Artificial intelligence in gastrointestinal surgery: A minireview of predictive models and clinical applications

Table 1 Studies summarizing the role of artificial intelligence in diagnosing gastrointestinal disorders and gastrointestinal malignancies

No.	Title of study	Ref.	Sample size	Validation method	Key limitations
1	Artificial intelligence differentiates abdominal Henoch-Schönlein purpura from acute appendicitis in children	Nie et al[15]	Paediatric cohort	External	Generalizability to broader populations unclear
2	Application of artificial intelligence in gastroenterology	Yang et al[16]	Various studies	Mixed internal and external	Diversity in study methodologies and validation approaches
3	Applications of artificial intelligence in digital pathology for gastric cancer	Chen et al[17]	N/A	External	Small sample sizes in studies, lack of clinical trial validation
4	Applications of artificial intelligence in screening, diagnosis, treatment, and prognosis of colorectal cancer	Qiu et al[18]	N/A	Mixed	Lack of long-term validation and diverse population samples
5	Artificial intelligence and acute appendicitis: A systematic review of diagnostic and prognostic models	Issaiy et al[19]	8 studies	Internal	Inconsistent diagnostic performance across studies
6	Artificial intelligence technique in detection of early esophageal cancer	Huang et al[20]	200+ patients	External	External validation required in diverse clinical settings
7	Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding	He et al[21]	500+ patients	Mixed	External validation not yet confirmed
8	Diagnostic performance of artificial intelligence-centred systems in the diagnosis and postoperative surveillance of upper gastrointestinal malignancies using computed tomography imaging: A systematic review	Christou et al[22]	20 studies	External	Limited validation on heterogeneous patient populations
9	Deep learning for prediction of lymph node metastasis in gastric cancer	Jin et al[23]	1000+ patients	External	Limited external validation in diverse populations
10	Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma	Calderaro et al[24]	100+ patients	Internal	Lack of large-scale, multi-center validation
11	Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio	Hong et al[25]	200+ gastric cancer patients	External	Single-center validation, lack of long-term clinical data
12	Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC	Li et al[26]	150+ patients	External	Need for further clinical validation with diverse cohorts
13	Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicenter retrospective diagnostic study	Tang et al[27]	500+ patients	External	Single-region data, limited external validation
14	Diagnostic performance of artificial intelligence-centred systems in the diagnosis and postoperative surveillance of upper gastrointestinal malignancies using computed tomography imaging	Chidambaram et al[28]	30 studies	External	Limited large-scale external validation

HCC: Hepatocellular carcinoma; N/A: Not applicable.

Table 2 Studies summarizing the role of artificial intelligence in response prediction and prognostication

No.	Title of study	Ref.	Sample size	Validation method	Key limitations
1	Machine learning in gastrointestinal surgery	Sakamoto et al[31]	N/A	Mixed internal and external	Lack of detailed validation cohort; small sample sizes
2	A histopathology-based AI system for genetic alteration screening in intrahepatic cholangiocarcinoma	Xiao et al[32]	100+ patients	Internal	Small sample size, lack of external validation
3	A nomogram based on a collagen feature SVM for predicting treatment response in rectal cancer	Jiang et al[33]	200+ patients	External	Single-center data, limited generalizability
4	A novel classification of intrahepatic cholangiocarcinoma phenotypes using ML	Tsilimigras et al[34]	150+ patients	External	Limited external validation in diverse populations
5	AI system for pathologic outcome prediction in early gastric cancer	Lee et al[36]	300+ patients	External	Lack of long-term follow-up data
6	AI-based recognition model for colorectal liver metastases in intraoperative ultrasound	Takayama et al[37]	100+ patients	External	Single-center validation, limited scalability
7	Analysis of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma	Wen et al[38]	200+ patients	External	Requires further clinical validation in diverse cohorts
8	AI and ML predicting transarterial chemoembolization outcomes	Cho et al[39]	100+ patients	Mixed internal and external	Inconsistent prediction performance across centers
9	AI based on serum biomarkers predicts efficacy of lenvatinib in hepatocellular carcinoma	Hsu et al[40]	100+ patients	Internal	Needs multi-center validation
10	AI-enabled histological prediction of remission or activity in ulcerative colitis	Iacucci et al[41]	150+ patients	External	Lack of long-term clinical outcomes, limited sample size
11	AI for lymph node metastasis prediction in gastric cancer	Yan et al[42]	500+ gastric cancer patients	External	Limited multi-center validation, needs wider cohort testing
12	AI in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies	Nagaraju et al[43]	N/A	N/A	Lacks detailed validation or large cohorts
13	AI in perioperative management of major gastrointestinal surgeries	Solanki et al[44]	300+ patients	External	Limited by single-center studies; needs broader cohort validation
14	AI system to determine risk of colorectal cancer metastasis	Kudo et al[45]	200+ patients	External	Validation needed in multi-center settings
15	AI-driven patient selection for preoperative portal vein embolization	Kuhn et al[46]	150+ patients	External	Requires larger-scale studies to confirm findings
16	Comparison of models for predicting quality of life after hepatocellular carcinoma surgery	Chiu et al[47]	100+ patients	Mixed internal and external	Limited external validation and long-term data
17	ML survival framework for pancreatic cancer	Wang et al[48]	300+ pancreatic cancer patients	Internal	Lack of multi-center validation; small sample size
18	Deep learning model for predicting hepatocellular carcinoma recurrence	Liu et al[86]	200+ patients	External	Need for larger cohort validation

AI: Artificial intelligence; ML: Machine learning; N/A: Not applicable; SVM: Support vector machine.

Table 3 Studies summarizing the role of artificial intelligence in predicting recurrence in gastrointestinal malignancies

No.	Title of study	Ref.	Sample size	Validation method	Key limitations
1	A machine learning predictive model for recurrence of resected distal cholangiocarcinoma	Perez et al[84]	654 patients	External	Limited to a single center, needs multi-center validation
2	A novel prediction model for colon cancer recurrence using auto-artificial intelligence	Mazaki et al[85]	500+ patients	Internal	Needs external validation across diverse cohorts
3	Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation	Liu et al[86]	500+ patients	External	Single-center validation, needs larger cohort testing
4	Deep learning model for predicting gastric cancer recurrence based on computed tomography imaging	Cao et al[87]	200+ patients	External	Needs further multi-center validation
5	Machine learning model to predict early recurrence of intrahepatic cholangiocarcinoma	Alaimo et al[88]	100+ patients	Internal	Requires external validation for broader applicability
6	Prognostic prediction model for elderly gastric cancer patients based on oxidative stress biomarkers	Zhang et al[89]	200+ elderly patients	External	Lack of multi-center data, small sample size
7	Consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer	Liu et al[90]	300+ patients	External	Validation needed in larger, multi-center trials
8	ML identifies autophagy-related genes as markers of recurrence in colorectal cancer	Wu et al[91]	200+ patients	External	Needs larger sample size and multi-center validation
9	ML models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture	Xia et al[92]	250+ patients	Internal	Inadequate external validation across different patient groups
10	ML prediction of early recurrence in gastric cancer: Nationwide real-world study	Zhang et al[93]	1500+ patients	External	Single-region data, needs validation in global populations
11	CT radiomics and ML predicts recurrence of hepatocellular carcinoma post-resection	Ji et al[94]	300+ patients	External	Needs more validation across different clinical settings
13	ML-based gene signature predicts paclitaxel survival benefit in gastric cancer	Sundar et al[95]	350+ patients	External	Needs external validation in diverse clinical settings
14	CT-based deep learning model for predicting early recurrence in gastric cancer	Guo et al[96]	200+ patients	External	Needs larger multi-center validation

CT: Computed tomography; ML: Machine learning.

Table 4 Studies summarizing the role of artificial intelligence in liver transplantation

No.	Title of study	Ref.	Sample size	Validation method	Key limitations
1	Knowledge domain and frontier trends of artificial intelligence applied in solid organ transplantation	Gong et al[110]	N/A	N/A	Lacks specific cohort data and external validation
2	Artificial intelligence and liver transplantation: Looking for the best donor-recipient pairing	Briceño et al[111]	200+ patients	External	Requires larger, multi-center validation
3	AI for predicting survival following deceased donor liver transplantation	Yu et al[112]	100+ patients	Internal	Needs broader validation in diverse populations
4	AI, ML, and deep learning in liver transplantation	Bhat et al[113]	N/A	Mixed	Lack of clear sample size and inconsistent external validation
5	Bibliometric and LDA analysis of acute rejection in liver transplantation	Jiang et al[114]	N/A	N/A	The study lacks clinical validation data and does not include real-world cohorts
6	Criteria and prognostic models for hepatocellular carcinoma patients undergoing liver transplantation	Sha et al[115]	150+ patients	External	Needs further multi-center validation and more diverse patient groups
7	Machine learning in liver transplantation: A tool for unsolved questions	Ferrarese et al[116]	N/A	Mixed	Lack of specific cohort validation, focuses mainly on theoretical models
8	ML model for predicting liver transplant outcomes in hepatitis C patients	Zabara et al[117]	100+ patients	Internal	Limited to one patient cohort, needs broader validation
9	Machine learning algorithms for predicting liver transplant results	Briceño et al[118]	150+ patients	External	Needs validation across different geographical regions and patient types
10	Supervised machine learning to predict hepatic immunological tolerance	Morita-Nakagawa et al[119]	50+ patients	Internal	Requires larger cohort size and further multi-center validation
11	AI for predicting survival of individual grafts in liver transplantation	Wingfield et al[120]	200+ patients	External	Validation needed in multi-center studies with diverse populations

AI: Artificial intelligence; ML: Machine learning; N/A: Not applicable; LDA: Latent Dirichlet allocation.