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

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.