Artificial intelligence in gastrointestinal surgery: A systematic review

Table 2 Overview of studies focusing on preoperative planning and risk prediction in gastrointestinal surgery

Ref.	Focus area	AI method/model	Key outcome	Number of data points	Results
Galvis-García et al[13], 2023	Colorectal polyp detection and classification	Deep learning, CNN, CAD (CADe/CADx)	Increased adenoma and polyp detection rates using AI assisted colonoscopy in real time	1038 patients (RCT), 8641 images (CNN study), 466 polyps (CADx), 238 lesions (endocytoscopy)	Sensitivity up to 96.5%, specificity up to 93%, accuracy up to 96.4%, F1 score approximately 94%, NPV up to 99.6%
Zhang et al[14], 2023	Diagnosis of choledocholithiasis in gallstone patients	Machine learning (7 models), AI (ModelArts)	Developed and validated AI model with high diagnostic accuracy for CBD stones prediction	1199 patients (681 with CBD stones)	ModelArts AI: Accuracy 0.97, recall 097, precision 0.971, F1 score 097; machine learning AUCs: 0.77-0.81
Ahmad et al[15], 2022	Detection of subtle and advanced colorectal neoplasia	Deep learning (ResNet 101 CNN)	High sensitivity for detecting flat lesions, sessile serrated lesions, and advanced colorectal polyps	173 polyps, 35114 polyp positive frames, 634988 polyp negative frames across multiple datasets	Per polyp sensitivity: 100%, 98.9%, and 79.5% (in subtle set); F1 score: Up to 87.9%; CNN outperformed expert and trainee endoscopists in detection speed and accuracy
Lei et al[16], 2023	Polyp detection via colon capsule endoscopy	Deep learning (CNN, AiSPEED™)	Feasibility and diagnostic accuracy of AI assisted reading compared to clinician interpretation	Target: 674 patients (597 needed for power; both prospective and retrospective recruitment)	Sensitivity/specificity of AI to be compared to clinician standard; exact results pending study is ongoing
Eckhoff et al[17], 2023	Surgical phase recognition for Ivor-Lewis esophagectomy	CNN + LSTM (TEsoNet), transfer learning	Demonstrated feasibility of knowledge transfer from sleeve gastrectomy to esophagectomy with moderate accuracy	60 sleeve gastrectomy videos, 40 esophagectomy videos (used in combinations across 5 experiments)	Single procedure accuracy: 87.7% (sleeve). Transfer learning: 23.4% overall (4 overlapping phases: 58.6%). Co training max accuracy: 40.8%
Blum et al[18], 2024	Prediction of choledocholithiasis	Logistic regression, RF, XGBoost, KNN; ensemble model	Machine learning models can outperform ASGE guidelines in predicting choledocholithiasis risk using pre MRCP data	222 patients	AUROC: 0.83 (RF), accuracy: 0.81 (ensemble), sensitivity: Up to 0.94, F1 score: Up to 0.82
Axon[19], 2020	Evolution and future of digestive endoscopy	Conceptual AI (future prediction)	Highlights AI’s potential to surpass expert level diagnostic accuracy and support real time treatment decisions		Predicts that AI will revolutionize diagnosis and treatment in endoscopy with real time support tools
Hsu et al[20], 2023	Predicting postoperative GIB after bariatric surgery	RF, XGBoost, NNs	Machine learning models outperform logistic regression in predicting GIB, aiding clinical decision making	159959 patients (632 with GIB)	RF AUROC: 0.764, sensitivity: 75.4%, specificity: 70.0%; XGBoost AUROC: 0.746; NN AUROC: 0.741; logistic regression AUROC: 0.709
Athanasiadis et al[21], 2025	Accuracy of self-assessment in laparoscopic cholecystectomy (CVS quality)		Surgeons frequently overestimate CVS performance; self-assessment alone is insufficient	25 surgeons enrolled, 13 submitted 1 video, 4 submitted 2 videos	No surgeon achieved adequate CVS per expert review; significant discrepancy between self and expert ratings on Strasberg scale
Bisschops et al[22], 2019	Advanced endoscopic imaging for colorectal neoplasia		Guidance on when to use advanced imaging (HD WLE, CE, NBI, etc.) for detection and differentiation of colorectal lesions		AI suggested for future use if validated; various imaging techniques reviewed for ADR, miss rates, lesion detection
Han et al[23], 2025	Intraoperative recognition of PAN during total mesorectal excision	DeepLabv3+ with ResNet50 backbone	AI model (AINS) achieved real time neuro recognition of PAN, aiding nerve preservation during rectal cancer surgery	1780 images (1424 training, 356 validation)	Accuracy: 0.9609, precision: 0.7494, recall: 0.6587, F1 score: 0.7011; AI outperformed surgeons (F1: 0.4568) and operated faster (3 minutes vs 25 minutes)

AI: Artificial intelligence; CNN: Convolutional neural network; CAD: Computer-aided diagnosis; RCT: Randomized controlled trial; NPV: Negative predictive value; CBD: Common bile duct; AUC: Area under the curve; LSTM: Long short-term memory; RF: Random forest; XGBoost: Extreme gradient boosting; KNN: K-nearest neighbors; ASGE: American Society for Gastrointestinal Endoscopy; MRCP: Magnetic resonance cholangiopancreatography; AUROC: Area under the receiver operating characteristic curve; NN: Neural network; GIB: Gastrointestinal bleeding; CVS: Clinical vignette scoring; HD WLE: High-definition white light endoscopy; CE: Chromoendoscopy; NBI: Narrow band imaging; ADR: Adverse drug reaction; PAN: Pelvic autonomic nerves.