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

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.