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.