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

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.