{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T15:50:13Z","timestamp":1794412213656,"version":"4.9.0"},"reference-count":156,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","gastrojournal.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Gastroenterology"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1053\/j.gastro.2025.04.011","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T03:54:20Z","timestamp":1745898860000},"page":"471-486","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"title":["Artificial Intelligence in Biliopancreatic Disorders: Applications in Cross-Sectional Imaging and Endoscopy"],"prefix":"10.1053","volume":"169","author":[{"given":"Carlos","family":"Robles-Medranda","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inez","family":"Verpalen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Schulz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Spadaccini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1053\/j.gastro.2025.04.011_bib1","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"key":"10.1053\/j.gastro.2025.04.011_bib2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"10.1053\/j.gastro.2025.04.011_bib3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.52787\/agl.v53i3.339","article-title":"La era de la endoscopia inteligente: c\u00f3mo la inteligencia artificial potencia la endoscopia digestiva","volume":"53","author":"Baquerizo-Burgos","year":"2023","journal-title":"Acta Gastroenterol Latinoam"},{"key":"10.1053\/j.gastro.2025.04.011_bib4","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.gie.2023.12.003","article-title":"Consensus statements on the current landscape of artificial intelligence applications in endoscopy, addressing roadblocks, and advancing artificial intelligence in gastroenterology","volume":"101","author":"Parasa","year":"2025","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib5","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1055\/a-1950-5694","article-title":"Expected value of artificial intelligence in gastrointestinal endoscopy: European Society of Gastrointestinal Endoscopy (ESGE) position statement","volume":"54","author":"Messmann","year":"2022","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib6","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1136\/gutjnl-2024-332820","article-title":"QUAIDE - Quality assessment of AI preclinical studies in diagnostic endoscopy","volume":"74","author":"Antonelli","year":"2024","journal-title":"Gut"},{"key":"10.1053\/j.gastro.2025.04.011_bib7","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.gie.2020.04.071","article-title":"Deep learning\u2013based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video)","volume":"92","author":"Zhang","year":"2020","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib8","doi-asserted-by":"crossref","first-page":"e303","DOI":"10.1016\/S2589-7500(20)30078-9","article-title":"Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation","volume":"2","author":"Liu","year":"2020","journal-title":"Lancet Digit Health"},{"key":"10.1053\/j.gastro.2025.04.011_bib9","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2024.22454","article-title":"Diagnosing solid lesions in the pancreas with multimodal artificial intelligence: a randomized crossover trial","volume":"7","author":"Cui","year":"2024","journal-title":"JAMA Netw Open"},{"key":"10.1053\/j.gastro.2025.04.011_bib10","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.3390\/jcm12113757","article-title":"A comprehensive guide to artificial intelligence in endoscopic ultrasound","volume":"12","author":"Khalaf","year":"2023","journal-title":"J Clin Med"},{"key":"10.1053\/j.gastro.2025.04.011_bib11","doi-asserted-by":"crossref","first-page":"156","DOI":"10.4103\/EUS-D-21-00063","article-title":"Pooled diagnostic parameters of artificial intelligence in EUS image analysis of the pancreas: a descriptive quantitative review","volume":"11","author":"Mohan","year":"2022","journal-title":"Endosc Ultrasound"},{"key":"10.1053\/j.gastro.2025.04.011_bib12","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.dld.2022.10.005","article-title":"Machine learning for the prediction of post-ERCP pancreatitis risk: a proof-of-concept study","volume":"55","author":"Archibugi","year":"2023","journal-title":"Dig Liver Dis"},{"key":"10.1053\/j.gastro.2025.04.011_bib13","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1007\/s00247-021-05114-8","article-title":"How does artificial intelligence in radiology improve efficiency and health outcomes?","volume":"52","author":"van Leeuwen","year":"2022","journal-title":"Pediatr Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib14","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1148\/rg.2021200210","article-title":"Deep learning: an update for radiologists","volume":"41","author":"Cheng","year":"2021","journal-title":"Radiographics"},{"key":"10.1053\/j.gastro.2025.04.011_bib15","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s40644-020-00311-4","article-title":"How to develop a meaningful radiomic signature for clinical use in oncologic patients","volume":"20","author":"Papanikolaou","year":"2020","journal-title":"Cancer Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib16","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1159\/000518020","article-title":"A 3D quantitative MRC modeling images detected case of intrahepatic biliary stricture diseases","volume":"15","author":"Nanashima","year":"2021","journal-title":"Case Rep Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib17","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1002\/jmri.27113","article-title":"Quantitative MRCP imaging: accuracy, repeatability, reproducibility, and cohort-derived normative ranges","volume":"52","author":"Goldfinger","year":"2020","journal-title":"J Magn Reson Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib18","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1007\/s00261-021-03378-0","article-title":"Associations between MRI T1 mapping, liver stiffness, quantitative MRCP, and laboratory biomarkers in children and young adults with autoimmune liver disease","volume":"47","author":"Mahalingam","year":"2022","journal-title":"Abdom Radiol (NY)"},{"key":"10.1053\/j.gastro.2025.04.011_bib19","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1002\/hep4.1860","article-title":"A quantitative magnetic resonance cholangiopancreatography metric of intrahepatic biliary dilatation severity detects high-risk primary sclerosing cholangitis","volume":"6","author":"Selvaraj","year":"2022","journal-title":"Hepatol Commun"},{"key":"10.1053\/j.gastro.2025.04.011_bib20","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1111\/apt.17944","article-title":"Quantitative MRCP and metrics of bile duct disease over time in patients with primary sclerosing cholangitis: a prospective study","volume":"59","author":"Trivedi","year":"2024","journal-title":"Aliment Pharmacol Ther"},{"key":"10.1053\/j.gastro.2025.04.011_bib21","article-title":"Quantitative magnetic resonance cholangiopancreatography metrics are associated with disease severity and outcomes in people with primary sclerosing cholangitis","volume":"4","author":"Cazzagon","year":"2022","journal-title":"JHEP Rep"},{"key":"10.1053\/j.gastro.2025.04.011_bib22","article-title":"A composite score using quantitative magnetic resonance cholangiopancreatography predicts clinical outcomes in primary sclerosing cholangitis","volume":"5","author":"Vuppalanchi","year":"2023","journal-title":"JHEP Rep"},{"key":"10.1053\/j.gastro.2025.04.011_bib23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s00330-021-08142-y","article-title":"Evaluation of quantitative MRCP (MRCP+) for risk stratification of primary sclerosing cholangitis: comparison with morphological MRCP, MR elastography, and biochemical risk scores","volume":"32","author":"Ismail","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib24","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.dld.2022.10.015","article-title":"A quantitative MRCP-derived score for medium-term outcome prediction in primary sclerosing cholangitis","volume":"55","author":"Cristoferi","year":"2023","journal-title":"Dig Liver Dis"},{"key":"10.1053\/j.gastro.2025.04.011_bib25","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.3390\/diagnostics13081488","article-title":"Update on the applications of radiomics in diagnosis, staging, and recurrence of intrahepatic cholangiocarcinoma","volume":"13","author":"Brunese","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib26","article-title":"Application of AI on cholangiocarcinoma","volume":"14","author":"Huang","year":"2024","journal-title":"Front Oncol"},{"key":"10.1053\/j.gastro.2025.04.011_bib27","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1001\/jama.2019.10306","article-title":"Making machine learning models clinically useful","volume":"322","author":"Shah","year":"2019","journal-title":"JAMA"},{"key":"10.1053\/j.gastro.2025.04.011_bib28","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1186\/s12876-023-02759-7","article-title":"Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data","volume":"23","author":"Hu","year":"2023","journal-title":"BMC Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib29","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.3390\/genes14051104","article-title":"EDLM: ensemble deep learning model to detect mutation for the early detection of cholangiocarcinoma","volume":"14","author":"Shah","year":"2023","journal-title":"Genes (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib30","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1007\/s11547-024-01878-9","article-title":"Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT","volume":"129","author":"Rocca","year":"2024","journal-title":"Radiol Med"},{"key":"10.1053\/j.gastro.2025.04.011_bib31","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1186\/s12880-024-01339-9","article-title":"A deep learning-powered diagnostic model for acute pancreatitis","volume":"24","author":"Zhang","year":"2024","journal-title":"BMC Med Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib32","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2024.1332387","article-title":"Predicting histologic grades for pancreatic neuroendocrine tumors by radiologic image-based artificial intelligence: a systematic review and meta-analysis","volume":"14","author":"Yan","year":"2024","journal-title":"Front Oncol"},{"key":"10.1053\/j.gastro.2025.04.011_bib33","doi-asserted-by":"crossref","first-page":"4644","DOI":"10.3390\/jcm13164644","article-title":"Diagnostics and management of pancreatic cystic lesions\u2014new techniques and guidelines","volume":"13","author":"Rogowska","year":"2024","journal-title":"J Clin Med"},{"key":"10.1053\/j.gastro.2025.04.011_bib34","doi-asserted-by":"crossref","DOI":"10.2196\/40702","article-title":"Deep learning to detect pancreatic cystic lesions on abdominal computed tomography scans: development and validation study","volume":"2","author":"Duh","year":"2023","journal-title":"JMIR AI"},{"key":"10.1053\/j.gastro.2025.04.011_bib35","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222778","article-title":"Pancreatic cyst surveillance: who, why, how?","volume":"308","author":"Luk","year":"2023","journal-title":"Radiology"},{"key":"10.1053\/j.gastro.2025.04.011_bib36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.14701\/ahbps.22-107","article-title":"IPMN-LEARN: a linear support vector machine learning model for predicting low-grade intraductal papillary mucinous neoplasms","volume":"27","author":"Hernandez-Barco","year":"2023","journal-title":"Ann Hepatobiliary Pancreat Surg"},{"key":"10.1053\/j.gastro.2025.04.011_bib37","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1007\/s00330-023-10158-5","article-title":"Radiomics model versus 2017 revised international consensus guidelines for predicting malignant intraductal papillary mucinous neoplasms","volume":"34","author":"Lee","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib38","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1093\/bjs\/znad201","article-title":"Artificial intelligence-based models to assess the risk of malignancy on radiological imaging in patients with intraductal papillary mucinous neoplasm of the pancreas: scoping review","volume":"110","author":"Balduzzi","year":"2023","journal-title":"Br J Surg"},{"key":"10.1053\/j.gastro.2025.04.011_bib39","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1148\/radiol.2511081296","article-title":"Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults","volume":"251","author":"Sodickson","year":"2009","journal-title":"Radiology"},{"key":"10.1053\/j.gastro.2025.04.011_bib40","doi-asserted-by":"crossref","first-page":"3033","DOI":"10.1038\/s41591-023-02640-w","article-title":"Large-scale pancreatic cancer detection via non-contrast CT and deep learning","volume":"29","author":"Cao","year":"2023","journal-title":"Nat Med"},{"key":"10.1053\/j.gastro.2025.04.011_bib41","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1097\/RLI.0000000000000992","article-title":"Automatic detection of pancreatic lesions and main pancreatic duct dilatation on portal venous CT scans using deep learning","volume":"58","author":"Abi Nader","year":"2023","journal-title":"Invest Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib42","article-title":"The FELIX project: deep networks to detect pancreatic neoplasms","author":"Xia","year":"2022","journal-title":"medRxiv"},{"key":"10.1053\/j.gastro.2025.04.011_bib43","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1053\/j.gastro.2023.08.034","article-title":"Automated artificial intelligence model trained on a large data set can detect pancreas cancer on diagnostic computed tomography scans as well as visually occult preinvasive cancer on prediagnostic computed tomography scans","volume":"165","author":"Korfiatis","year":"2023","journal-title":"Gastroenterology"},{"key":"10.1053\/j.gastro.2025.04.011_bib44","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1055\/a-1850-6717","article-title":"An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation","volume":"55","author":"Huang","year":"2023","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib45","doi-asserted-by":"crossref","DOI":"10.1002\/deo2.267","article-title":"Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography","volume":"4","author":"Kuwahara","year":"2024","journal-title":"DEN Open"},{"key":"10.1053\/j.gastro.2025.04.011_bib46","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-28058-5","article-title":"Diagnosing malignant distal bile duct obstruction using artificial intelligence based on clinical biomarkers","volume":"13","author":"Sugimoto","year":"2023","journal-title":"Sci Rep"},{"key":"10.1053\/j.gastro.2025.04.011_bib47","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1007\/s00464-021-08491-1","article-title":"Pre- and post-procedure risk prediction models for post-endoscopic retrograde cholangiopancreatography pancreatitis","volume":"36","author":"Park","year":"2022","journal-title":"Surg Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib48","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.gie.2024.03.033","article-title":"Multistep validation of a post-ERCP pancreatitis prediction system integrating multimodal data: a multicenter study","volume":"100","author":"Xu","year":"2024","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib49","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0276278","article-title":"The application of artificial intelligence to support biliary atresia screening by ultrasound images: a study based on deep learning models","volume":"17","author":"Hsu","year":"2022","journal-title":"PLoS One"},{"key":"10.1053\/j.gastro.2025.04.011_bib50","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1007\/s10620-021-06961-z","article-title":"Digital cholangioscopic interpretation: when north meets the south","volume":"67","author":"Kahaleh","year":"2021","journal-title":"Dig Dis Sci"},{"key":"10.1053\/j.gastro.2025.04.011_bib51","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1055\/a-0607-2534","article-title":"Reliability and accuracy of a novel classification system using peroral cholangioscopy for the diagnosis of bile duct lesions","volume":"50","author":"Robles-Medranda","year":"2018","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib52","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.gie.2020.07.025","article-title":"Vascularity can distinguish neoplastic from non-neoplastic bile duct lesions during digital single-operator cholangioscopy","volume":"93","author":"Robles-Medranda","year":"2021","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib53","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.gie.2021.08.015","article-title":"Digital single-operator cholangioscopy interobserver study using a new classification: the Mendoza Classification (with video)","volume":"95","author":"Kahaleh","year":"2022","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib54","doi-asserted-by":"crossref","first-page":"e94","DOI":"10.1097\/MCG.0000000000001321","article-title":"Digital single-operator cholangioscopy (DSOC) improves interobserver agreement (IOA) and accuracy for evaluation of indeterminate biliary strictures: the Monaco classification","volume":"56","author":"Sethi","year":"2022","journal-title":"J Clin Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib55","doi-asserted-by":"crossref","DOI":"10.14309\/ctg.0000000000000418","article-title":"Automatic identification of papillary projections in indeterminate biliary strictures using digital single-operator cholangioscopy","volume":"12","author":"Ribeiro","year":"2021","journal-title":"Clin Transl Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib56","doi-asserted-by":"crossref","first-page":"4827","DOI":"10.3390\/cancers15194827","article-title":"Deep learning for automatic diagnosis and morphologic characterization of malignant biliary strictures using digital cholangioscopy: a multicentric study","volume":"15","author":"Saraiva","year":"2023","journal-title":"Cancers (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib57","doi-asserted-by":"crossref","first-page":"E262","DOI":"10.1055\/a-1723-3369","article-title":"Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy","volume":"10","author":"Pereira","year":"2022","journal-title":"Endosc Int Open"},{"key":"10.1053\/j.gastro.2025.04.011_bib58","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.gie.2021.08.027","article-title":"Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study","volume":"95","author":"Saraiva","year":"2022","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib59","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.gie.2022.08.021","article-title":"Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video)","volume":"97","author":"Marya","year":"2022","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib60","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.gie.2023.02.026","article-title":"A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos)","volume":"98","author":"Zhang","year":"2023","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib61","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1055\/a-2034-3803","article-title":"Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicentric validation of a convolutional neural network model","volume":"55","author":"Robles-Medranda","year":"2023","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib62","doi-asserted-by":"crossref","first-page":"E1118","DOI":"10.1055\/a-2404-5699","article-title":"Cholangioscopy-based convoluted neuronal network vs confocal laser endomicroscopy in identification of neoplastic biliary strictures","volume":"12","author":"Robles-Medranda","year":"2024","journal-title":"Endosc Int Open"},{"key":"10.1053\/j.gastro.2025.04.011_bib63","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1111\/jgh.16137","article-title":"Methods of tissue preparation after EUS-guided tissue acquisition without rapid on-site assessment: results of a randomized study","volume":"38","author":"Phan","year":"2023","journal-title":"J Gastroenterol Hepatol"},{"key":"10.1053\/j.gastro.2025.04.011_bib64","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2021.103238","article-title":"A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound","volume":"65","author":"Yao","year":"2021","journal-title":"EBioMedicine"},{"key":"10.1053\/j.gastro.2025.04.011_bib65","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1109\/TMI.2021.3139023","article-title":"Voice-assisted image labeling for endoscopic ultrasound classification using neural networks","volume":"41","author":"Bonmati","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib66","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1080\/21681163.2022.2154274","article-title":"Automatic pancreas anatomical part detection in endoscopic ultrasound videos","volume":"11","author":"Fleurentin","year":"2023","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"key":"10.1053\/j.gastro.2025.04.011_bib67","doi-asserted-by":"crossref","first-page":"e812","DOI":"10.1016\/S2589-7500(23)00160-7","article-title":"Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial","volume":"5","author":"Wu","year":"2023","journal-title":"Lancet Digit Health"},{"key":"10.1053\/j.gastro.2025.04.011_bib68","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.gie.2023.10.028","article-title":"Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos)","volume":"99","author":"Robles-Medranda","year":"2024","journal-title":"Gastroint Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib69","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1097\/JS9.0000000000000995","article-title":"Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study","volume":"110","author":"Tian","year":"2024","journal-title":"Int J Surg"},{"key":"10.1053\/j.gastro.2025.04.011_bib70","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1016\/j.gie.2024.10.030","article-title":"A deep learning-based, real-time image report system for linear EUS","volume":"101","author":"Li","year":"2025","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib71","doi-asserted-by":"crossref","first-page":"AB813","DOI":"10.1016\/j.gie.2024.04.1961","article-title":"Comparative evaluation of artificial intelligence and endoscopists\u2019 accuracy in endoscopic ultrasound for identifying normal anatomical structures: a multi-institutional, cross-sectional study","volume":"99","author":"Robles-Medranda","year":"2024","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib72","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1136\/gutjnl-2020-322821","article-title":"Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis","volume":"70","author":"Marya","year":"2021","journal-title":"Gut"},{"key":"10.1053\/j.gastro.2025.04.011_bib73","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1002\/jhbp.825","article-title":"Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study","volume":"28","author":"Tonozuka","year":"2021","journal-title":"J Hepatobiliary Pancreat Sci"},{"key":"10.1053\/j.gastro.2025.04.011_bib74","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0251701","article-title":"Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model","volume":"16","author":"Udri\u015ftoiu","year":"2021","journal-title":"PLoS One"},{"key":"10.1053\/j.gastro.2025.04.011_bib75","series-title":"Medical imaging 2022: ultrasonic imaging and tomography","first-page":"17","article-title":"Automatic detection of pancreatic tumors in endoscopic ultrasound videos using deep learning techniques","author":"Jaramillo","year":"2022"},{"key":"10.1053\/j.gastro.2025.04.011_bib76","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.973652","article-title":"Deep learning for real-time auxiliary diagnosis of pancreatic cancer in endoscopic ultrasonography","volume":"12","author":"Tian","year":"2022","journal-title":"Front Oncol"},{"key":"10.1053\/j.gastro.2025.04.011_bib77","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1055\/a-1873-7920","article-title":"Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses","volume":"55","author":"Kuwahara","year":"2023","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib78","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104591","article-title":"Bimodal segmentation and classification of endoscopic ultrasonography images for solid pancreatic tumor","volume":"83","author":"Ren","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"10.1053\/j.gastro.2025.04.011_bib79","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/TMI.2023.3289859","article-title":"DSMT-Net: dual self-supervised multi-operator transformation for multi-source endoscopic ultrasound diagnosis","volume":"43","author":"Li","year":"2024","journal-title":"IEEE Transact Med Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib80","doi-asserted-by":"crossref","DOI":"10.14309\/ctg.0000000000000771","article-title":"Deep learning and automatic differentiation of pancreatic lesions in endoscopic ultrasound: a transatlantic study","volume":"15","author":"Saraiva","year":"2024","journal-title":"Clin Transl Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib81","doi-asserted-by":"crossref","first-page":"p1020","DOI":"10.1016\/j.gie.2024.10.013","article-title":"A convolutional neural network-based system for identifying neuroendocrine neoplasm and multiple types of lesions in the pancreas using EUS (with videos)","volume":"101","author":"Ni","year":"2025","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib82","doi-asserted-by":"crossref","first-page":"4197","DOI":"10.1002\/mp.16390","article-title":"Endoscopic ultrasound diagnosis system based on deep learning in images capture and segmentation training of solid pancreatic masses","volume":"50","author":"Tang","year":"2023","journal-title":"Med Phys"},{"key":"10.1053\/j.gastro.2025.04.011_bib83","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1111\/cyt.13237","article-title":"Rapid on-site evaluation improves the sensitivity of endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) for solid pancreatic lesions irrespective of technique: a single-centre experience","volume":"34","author":"Milluzzo","year":"2023","journal-title":"Cytopathology"},{"key":"10.1053\/j.gastro.2025.04.011_bib84","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1111\/jgh.16073","article-title":"Application of artificial intelligence to digital-rapid on-site cytopathology evaluation during endoscopic ultrasound-guided fine needle aspiration: a proof-of-concept study","volume":"38","author":"Lin","year":"2023","journal-title":"J Gastroenterol Hepatol"},{"key":"10.1053\/j.gastro.2025.04.011_bib85","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2022.104022","article-title":"A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of pancreatic masses: a retrospective, multicenter, diagnostic study","volume":"80","author":"Zhang","year":"2022","journal-title":"EBioMedicine"},{"key":"10.1053\/j.gastro.2025.04.011_bib86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.14309\/ctg.0000000000000045","article-title":"Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas","volume":"10","author":"Kuwahara","year":"2019","journal-title":"Clin Transl Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib87","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.3390\/diagnostics11061052","article-title":"Deep learning-based differentiation between mucinous cystic neoplasm and serous cystic neoplasm in the pancreas using endoscopic ultrasonography","volume":"11","author":"Nguon","year":"2021","journal-title":"Diagnostics (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib88","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.gie.2020.12.054","article-title":"High performance in risk stratification of intraductal papillary mucinous neoplasms by confocal laser endomicroscopy image analysis with convolutional neural networks (with video)","volume":"94","author":"Machicado","year":"2021","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib89","doi-asserted-by":"crossref","first-page":"2041","DOI":"10.3390\/diagnostics12092041","article-title":"Deep learning for automatic differentiation of mucinous versus non-mucinous pancreatic cystic lesions: a pilot study","volume":"12","author":"Vilas-Boas","year":"2022","journal-title":"Diagnostics"},{"key":"10.1053\/j.gastro.2025.04.011_bib90","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1055\/a-1971-1274","article-title":"Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning","volume":"55","author":"Schulz","year":"2023","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib91","doi-asserted-by":"crossref","first-page":"3548","DOI":"10.1111\/jgh.15673","article-title":"Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions","volume":"36","author":"Jang","year":"2021","journal-title":"J Gastroenterol Hepatol"},{"key":"10.1053\/j.gastro.2025.04.011_bib92","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.gie.2020.08.024","article-title":"Application of artificial intelligence using a novel EUS-based convolutional neural network model to identify and distinguish benign and malignant hepatic masses","volume":"93","author":"Marya","year":"2021","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib93","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2024.102995","article-title":"An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor","volume":"13","author":"Ghorpade","year":"2024","journal-title":"MethodsX"},{"key":"10.1053\/j.gastro.2025.04.011_bib94","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41747-023-00419-9","article-title":"Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer","volume":"8","author":"Bereska","year":"2024","journal-title":"Eur Radiol Exp"},{"key":"10.1053\/j.gastro.2025.04.011_bib95","doi-asserted-by":"crossref","first-page":"3198","DOI":"10.3390\/diagnostics13203198","article-title":"Computed tomography-based radiomics using tumor and vessel features to assess resectability in cancer of the pancreatic head","volume":"13","author":"Litjens","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib96","article-title":"Artificial intelligence in biliopancreatic endoscopy: is there any role?","volume":"52\u201353","author":"Ahmad","year":"2021","journal-title":"Best Pract Res Clin Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib97","doi-asserted-by":"crossref","first-page":"555","DOI":"10.14309\/ajg.0000000000000565","article-title":"Use of artificial intelligence to reduce radiation exposure at fluoroscopy-guided endoscopic procedures","volume":"115","author":"Bang","year":"2020","journal-title":"Am J Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib98","doi-asserted-by":"crossref","first-page":"379","DOI":"10.3390\/cancers17030379","article-title":"Impact of artificial intelligence on pancreaticobiliary endoscopy","volume":"17","author":"Jain","year":"2025","journal-title":"Cancers (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib99","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108205","article-title":"Application of artificial intelligence in pancreas endoscopic ultrasound imaging-a systematic review","volume":"250","author":"Rousta","year":"2024","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1053\/j.gastro.2025.04.011_bib100","doi-asserted-by":"crossref","DOI":"10.1016\/j.bpg.2025.101975","article-title":"Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: current advances and future prospects","volume":"74","author":"Tacelli","year":"2025","journal-title":"Best Pract Res Clin Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib101","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.vgie.2023.06.003","article-title":"Usefulness of artificial intelligence-assisted digital single-operator cholangioscopy as a second-opinion consultation tool during interhospital assessment of an indeterminate biliary stricture: a case report","volume":"8","author":"Ricaurte-Ciro","year":"2023","journal-title":"VideoGIE"},{"key":"10.1053\/j.gastro.2025.04.011_bib102","author":"Uuk"},{"key":"10.1053\/j.gastro.2025.04.011_bib103","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1186\/s12957-024-03516-0","article-title":"Impact of imaging-diagnosed sarcopenia on outcomes in patients with biliary tract cancer after surgical resection: a systematic review and meta-analysis","volume":"22","author":"Ji","year":"2024","journal-title":"World J Surg Oncol"},{"key":"10.1053\/j.gastro.2025.04.011_bib104","doi-asserted-by":"crossref","first-page":"3274","DOI":"10.1080\/01635581.2022.2074063","article-title":"Sarcopenia predicts relevant clinical outcomes in biliary tract cancer patients: a systematic review and meta-analysis","volume":"74","author":"Yang","year":"2022","journal-title":"Nutr Cancer"},{"key":"10.1053\/j.gastro.2025.04.011_bib105","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.pan.2022.12.005","article-title":"Early prediction of the severe course, survival, and ICU requirements in acute pancreatitis by artificial intelligence","volume":"23","author":"\u0130nce","year":"2023","journal-title":"Pancreatology"},{"key":"10.1053\/j.gastro.2025.04.011_bib106","doi-asserted-by":"crossref","first-page":"4408","DOI":"10.1007\/s00330-018-5824-1","article-title":"Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis","volume":"29","author":"Chen","year":"2019","journal-title":"Eur Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib107","doi-asserted-by":"crossref","first-page":"e68","DOI":"10.1097\/SLA.0000000000005465","article-title":"Deep learning for fully automated prediction of overall survival in patients undergoing resection for pancreatic cancer","volume":"278","author":"Yao","year":"2023","journal-title":"Ann Surg"},{"key":"10.1053\/j.gastro.2025.04.011_bib108","doi-asserted-by":"crossref","first-page":"1742","DOI":"10.3390\/genes14091742","article-title":"An automated prognostic model for pancreatic ductal adenocarcinoma","volume":"14","author":"Vezakis","year":"2023","journal-title":"Genes (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib109","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.3390\/diagnostics13182889","article-title":"A review of the clinical applications of artificial intelligence in abdominal imaging","volume":"13","author":"Mervak","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib110","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00330-023-10116-1","article-title":"AI-based approaches in the daily practice of abdominal imaging","volume":"34","author":"Schmidt","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib111","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1053\/j.gastro.2023.04.003","article-title":"PANCAIM Consortium. Artificial intelligence in pancreatic ductal adenocarcinoma imaging: a commentary on potential future applications","volume":"165","author":"Schuurmans","year":"2023","journal-title":"Gastroenterology"},{"key":"10.1053\/j.gastro.2025.04.011_bib112","article-title":"Learn2Agree: fitting with multiple annotators without objective ground truth","author":"Wang","year":"2021","journal-title":"arXiv"},{"key":"10.1053\/j.gastro.2025.04.011_bib113","article-title":"A soft STAPLE algorithm combined with anatomical knowledge","author":"Kats","year":"2019","journal-title":"arXiv"},{"key":"10.1053\/j.gastro.2025.04.011_bib114","doi-asserted-by":"crossref","DOI":"10.1148\/rg.230067","article-title":"Understanding and mitigating bias in imaging artificial intelligence","volume":"44","author":"Tejani","year":"2024","journal-title":"Radiographics"},{"key":"10.1053\/j.gastro.2025.04.011_bib115","doi-asserted-by":"crossref","first-page":"e815","DOI":"10.1016\/S2589-7500(24)00154-7","article-title":"Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge","volume":"6","author":"Ma","year":"2024","journal-title":"Lancet Digit Health"},{"key":"10.1053\/j.gastro.2025.04.011_bib116","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.pan.2023.05.008","article-title":"Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs","volume":"23","author":"Mukherjee","year":"2023","journal-title":"Pancreatology"},{"key":"10.1053\/j.gastro.2025.04.011_bib117","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2024.105413","article-title":"Can I trust my fake data - a comprehensive quality assessment framework for synthetic tabular data in healthcare","volume":"185","author":"Vallevik","year":"2024","journal-title":"Int J Med Inform"},{"key":"10.1053\/j.gastro.2025.04.011_bib118","first-page":"75","article-title":"Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects","volume":"31","author":"Ko\u00e7ak","year":"2025","journal-title":"Diagn Interv Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib119","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1186\/s13244-024-01833-2","article-title":"Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology","volume":"15","author":"Sourlos","year":"2024","journal-title":"Insights Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib120","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.1038\/s41591-023-02608-w","article-title":"The value of standards for health datasets in artificial intelligence-based applications","volume":"29","author":"Arora","year":"2023","journal-title":"Nat Med"},{"key":"10.1053\/j.gastro.2025.04.011_bib121","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1016\/j.pan.2021.03.016","article-title":"Quality gaps in public pancreas imaging datasets: implications & challenges for AI applications","volume":"21","author":"Suman","year":"2021","journal-title":"Pancreatology"},{"key":"10.1053\/j.gastro.2025.04.011_bib122","author":"Alves"},{"key":"10.1053\/j.gastro.2025.04.011_bib123","article-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","author":"Simpson","year":"2019","journal-title":"arXiv"},{"key":"10.1053\/j.gastro.2025.04.011_bib124","unstructured":"National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). CPTAC-PDA. The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection, 2018."},{"key":"10.1053\/j.gastro.2025.04.011_bib125","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.jacr.2022.03.016","article-title":"Federated learning in medical imaging: part II: methods, challenges, and considerations","volume":"19","author":"Darzidehkalani","year":"2022","journal-title":"J Am Coll Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib126","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1186\/s13244-024-01801-w","article-title":"Impact of AI on radiology: a EuroAIM\/EuSoMII 2024 survey among members of the European Society of Radiology","volume":"15","author":"Zanardo","year":"2024","journal-title":"Insights Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib127","doi-asserted-by":"crossref","first-page":"8117","DOI":"10.1038\/s41467-024-52446-8","article-title":"ENCORE: a practical implementation to improve reproducibility and transparency of computational research","volume":"15","author":"van Kampen","year":"2024","journal-title":"Nat Commun"},{"key":"10.1053\/j.gastro.2025.04.011_bib128","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41746-024-01196-4","article-title":"The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review","volume":"7","author":"Schwabe","year":"2024","journal-title":"NPJ Digit Med"},{"key":"10.1053\/j.gastro.2025.04.011_bib129","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.240300","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol Artif Intell"},{"key":"10.1053\/j.gastro.2025.04.011_bib130","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2022.929453","article-title":"You can\u2019t have AI both ways: balancing health data privacy and access fairly","volume":"13","author":"Bak","year":"2022","journal-title":"Front Genet"},{"key":"10.1053\/j.gastro.2025.04.011_bib131","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.csbj.2024.02.014","article-title":"Privacy-preserving federated machine learning on FAIR health data: a real-world application","volume":"24","author":"Sinaci","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"10.1053\/j.gastro.2025.04.011_bib132","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41746-021-00385-9","article-title":"Do as AI say: susceptibility in deployment of clinical decision-aids","volume":"4","author":"Gaube","year":"2021","journal-title":"NPJ Dig Med"},{"key":"10.1053\/j.gastro.2025.04.011_bib133","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2021.1276","article-title":"Association of clinician diagnostic performance with machine learning\u2013based decision support systems","volume":"4","author":"Vasey","year":"2021","journal-title":"JAMA Netw Open"},{"key":"10.1053\/j.gastro.2025.04.011_bib134","article-title":"Prospective evaluation of artificial intelligence triage of intracranial hemorrhage on noncontrast head CT examinations","volume":"223","author":"Savage","year":"2024","journal-title":"AJR Am J Roentgenol"},{"key":"10.1053\/j.gastro.2025.04.011_bib135","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1016\/j.jacr.2020.09.045","article-title":"Evaluating artificial intelligence systems to guide purchasing decisions","volume":"17","author":"Filice","year":"2020","journal-title":"J Am Coll Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib136","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.acra.2021.09.002","article-title":"FDA-regulated AI algorithms: trends, strengths, and gaps of validation studies","volume":"29","author":"Ebrahimian","year":"2022","journal-title":"Acad Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib137","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1007\/s00330-023-09991-5","article-title":"Clinical use of artificial intelligence products for radiology in the Netherlands between 2020 and 2022","volume":"34","author":"van Leeuwen","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib138","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1093\/jamia\/ocaa268","article-title":"Trust and medical AI: the challenges we face and the expertise needed to overcome them","volume":"28","author":"Quinn","year":"2021","journal-title":"J Am Med Inform Assoc"},{"key":"10.1053\/j.gastro.2025.04.011_bib139","article-title":"Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe","volume":"5","author":"Walsh","year":"2023","journal-title":"BJR Open"},{"key":"10.1053\/j.gastro.2025.04.011_bib140","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13244-023-01586-4","article-title":"A holistic approach to implementing artificial intelligence in radiology","volume":"15","author":"Kim","year":"2024","journal-title":"Insights Imaging"},{"key":"10.1053\/j.gastro.2025.04.011_bib141","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2022.104828","article-title":"The 2021 landscape of FDA-approved artificial intelligence\/machine learning-enabled medical devices: an analysis of the characteristics and intended use","volume":"165","author":"Zhu","year":"2022","journal-title":"Int J Med Inform"},{"key":"10.1053\/j.gastro.2025.04.011_bib142","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102801","article-title":"Transformer guided progressive fusion network for 3D pancreas and pancreatic mass segmentation","volume":"86","author":"Qu","year":"2023","journal-title":"Med Image Anal"},{"key":"10.1053\/j.gastro.2025.04.011_bib143","series-title":"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS","first-page":"475","article-title":"Neural transformers for intraductal papillary mucosal neoplasms (IPMN) classification in MRI images","author":"Salanitri","year":"2022"},{"key":"10.1053\/j.gastro.2025.04.011_bib144","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1097\/MCG.0000000000001915","article-title":"An accurate and automated method for adenoma detection rate and report card generation utilizing common electronic health records","volume":"58","author":"Ganguly","year":"2023","journal-title":"J Clin Gastroenterol"},{"key":"10.1053\/j.gastro.2025.04.011_bib145","doi-asserted-by":"crossref","DOI":"10.2196\/66648","article-title":"Large language models in gastroenterology: systematic review","volume":"26","author":"Gong","year":"2024","journal-title":"J Med Internet Res"},{"key":"10.1053\/j.gastro.2025.04.011_bib146","doi-asserted-by":"crossref","first-page":"3328","DOI":"10.3390\/cancers16193328","article-title":"The application of large language models in gastroenterology: a review of the literature","volume":"16","author":"Maida","year":"2024","journal-title":"Cancers (Basel)"},{"key":"10.1053\/j.gastro.2025.04.011_bib147","article-title":"Artificial intelligence for cancer screening and surveillance","volume":"5","author":"Gentile","year":"2024","journal-title":"ESMO Real World Data Dig Oncol"},{"key":"10.1053\/j.gastro.2025.04.011_bib148","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222044","article-title":"Opportunistic screening: radiology scientific expert panel","volume":"307","author":"Pickhardt","year":"2023","journal-title":"Radiology"},{"key":"10.1053\/j.gastro.2025.04.011_bib149","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.241490","article-title":"Emerging AI autonomy: reducing the burden of unremarkable examinations","volume":"312","author":"Yoon","year":"2024","journal-title":"Radiology"},{"key":"10.1053\/j.gastro.2025.04.011_bib150","doi-asserted-by":"crossref","first-page":"7255","DOI":"10.1007\/s00330-024-10794-5","article-title":"Performance of AI to exclude normal chest radiographs to reduce radiologists\u2019 workload","volume":"34","author":"Schalekamp","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib151","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3348\/kjr.2021.0912","article-title":"Research highlight: artificial intelligence for ruling out negative examinations in screening breast MRI","volume":"23","author":"Youk","year":"2022","journal-title":"Korean J Radiol"},{"key":"10.1053\/j.gastro.2025.04.011_bib152","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1055\/s-0043-103411","article-title":"Performance measures for lower gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative","volume":"49","author":"Kaminski","year":"2017","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib153","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1055\/a-0749-8767","article-title":"Performance measures for ERCP and endoscopic ultrasound: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative","volume":"50","author":"Domagk","year":"2018","journal-title":"Endoscopy"},{"key":"10.1053\/j.gastro.2025.04.011_bib154","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.gie.2014.07.056","article-title":"Quality indicators for ERCP","volume":"81","author":"Adler","year":"2015","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib155","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.gie.2024.04.2905","article-title":"Quality indicators for colonoscopy","volume":"100","author":"Rex","year":"2024","journal-title":"Gastrointest Endosc"},{"key":"10.1053\/j.gastro.2025.04.011_bib156","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.973999","article-title":"Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis","volume":"12","author":"Yin","year":"2022","journal-title":"Front Oncol"}],"container-title":["Gastroenterology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0016508525006481?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0016508525006481?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T09:54:27Z","timestamp":1762163667000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0016508525006481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":156,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["S0016508525006481"],"URL":"https:\/\/doi.org\/10.1053\/j.gastro.2025.04.011","relation":{},"ISSN":["0016-5085"],"issn-type":[{"value":"0016-5085","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial Intelligence in Biliopancreatic Disorders: Applications in Cross-Sectional Imaging and Endoscopy","name":"articletitle","label":"Article Title"},{"value":"Gastroenterology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1053\/j.gastro.2025.04.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 by the AGA Institute.","name":"copyright","label":"Copyright"}]}}