{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:38:25Z","timestamp":1785411505774,"version":"3.56.0"},"reference-count":76,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82470700"],"award-info":[{"award-number":["82470700"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["82470700"],"award-info":[{"award-number":["82470700"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Gastric cancer is a globally prevalent malignancy, with early detection being pivotal for improving patient survival. While endoscopy remains the diagnostic gold standard, it frequently faces challenges such as missed lesions and operator dependency. Artificial intelligence (AI) has emerged as a powerful tool to address these limitations. This narrative review synthesizes recent evidence from PubMed and Web of Science, focusing on four core functional domains of AI-assisted gastric endoscopy: lesion detection and characterization, margin delineation, invasion depth prediction, and blind-spot monitoring. Furthermore, we summarize current limitations, including single-center data biases and algorithmic \u201cblack-box\u201d issues, and discuss future directions such as multimodal data integration and real-time video analysis systems. Ultimately, carefully validated AI represents a vital clinical adjunct that holds great potential to significantly enhance diagnostic accuracy and patient outcomes.<\/jats:p>","DOI":"10.3390\/diagnostics16142196","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:05:58Z","timestamp":1784077558000},"page":"2196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Artificial Intelligence in the Endoscopic Diagnosis of Gastric Cancer and Precancerous Lesions"],"prefix":"10.3390","volume":"16","author":[{"given":"Mengmeng","family":"Su","sequence":"first","affiliation":[{"name":"Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyang","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanying","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiming","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ref_1","first-page":"100","article-title":"Epidemiology of gastric cancer: Global trends, risk factors and premalignant conditions","volume":"13","author":"Grantham","year":"2023","journal-title":"J. Community Hosp. Intern. Med. Perspect."},{"key":"ref_2","first-page":"1","article-title":"Global, regional and national burden of stomach cancer and its prediction in 25 years: A cross-sectional study of the global burden of disease study 2021","volume":"86","author":"Li","year":"2005","journal-title":"Br. J. Hosp. Med."},{"key":"ref_3","unstructured":"Professional Committee on Gastric Cancer of Shanghai Anti-Cancer Association, and Professional Committee on Gastrointestinal Oncology of China Association for Promotion of Human Health Technology (2024). Chinese expert consensus on clinical practice of locally advanced gastric cancer invading adjacent organs (2024 edition). Zhongguo Aizheng Zazhi, 34, 517\u2013526."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/S0140-6736(17)33326-3","article-title":"Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): Analysis of individual records for 37,513,025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries","volume":"391","author":"Allemani","year":"2018","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1136\/gutjnl-2018-317556","article-title":"Development and validation of a prediction rule for estimating gastric cancer risk in the Chinese high-risk population: A nationwide multicentre study","volume":"68","author":"Cai","year":"2019","journal-title":"Gut"},{"key":"ref_6","first-page":"6735","article-title":"Human gastric carcinogenesis: A multistep and multifactorial process\u2014First American cancer society award lecture on cancer epidemiology and prevention","volume":"52","author":"Correa","year":"1992","journal-title":"Cancer Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1007\/s00761-024-01605-z","article-title":"Endoscopic diagnosis and treatment of gastric cancer","volume":"30","author":"Probst","year":"2024","journal-title":"Die Onkol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Martins, B.C., Moura, R.N., Kum, A.S.T., Matsubayashi, C.O., Marques, S.B., and Safatle-Ribeiro, A.V. (2023). Endoscopic imaging for the diagnosis of neoplastic and pre-neoplastic conditions of the stomach. Cancers, 15.","DOI":"10.3390\/cancers15092445"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.bpg.2015.05.010","article-title":"Advanced endoscopic imaging for early gastric cancer","volume":"29","author":"Kaise","year":"2015","journal-title":"Best Pract. Res. Clin. Gastroenterol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1055\/s-0035-1569580","article-title":"The european society of gastrointestinal endoscopy quality improvement initiative: Developing performance measures","volume":"48","author":"Rutter","year":"2015","journal-title":"Endoscopy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1016\/j.dld.2019.12.146","article-title":"Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence","volume":"52","author":"Zhang","year":"2020","journal-title":"Dig. Liver Dis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9444","DOI":"10.1007\/s00464-022-09420-6","article-title":"Artificial intelligence for diagnosing gastric lesions under white-light endoscopy","volume":"36","author":"Yuan","year":"2022","journal-title":"Surg. Endosc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/S1470-2045(19)30637-0","article-title":"Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: A multicentre, case-control, diagnostic study","volume":"20","author":"Luo","year":"2019","journal-title":"Lancet Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/s41746-023-00813-y","article-title":"Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy","volume":"6","author":"Dong","year":"2023","journal-title":"npj Digit. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.suronc.2020.11.017","article-title":"NBI utility in oncologic surgery: An organ by organ review","volume":"36","author":"Tirelli","year":"2021","journal-title":"Surg. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1117\/1.1695563","article-title":"Appearance of enhanced tissue features in narrow-band endoscopic imaging","volume":"9","author":"Gono","year":"2004","journal-title":"J. Biomed. Opt."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5534","DOI":"10.21037\/qims-24-482","article-title":"Diagnostic performance of white light endoscopy, narrow band imaging, and flexible spectral imaging color enhancement for early gastric cancer: A systematic review and meta-analysis","volume":"15","author":"Liu","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1055\/s-0042-108435","article-title":"A multicenter prospective study of the real-time use of narrow-band imaging in the diagnosis of premalignant gastric conditions and lesions","volume":"48","author":"Lage","year":"2016","journal-title":"Endoscopy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7800","DOI":"10.1007\/s00464-022-09319-2","article-title":"A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy","volume":"36","author":"Tang","year":"2022","journal-title":"Surg. Endosc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2523565","DOI":"10.1080\/07853890.2025.2523565","article-title":"Virtual NBI image synthesis using stable diffusion for enhanced recognition of early gastric cancer: A technical validation study","volume":"57","author":"Lei","year":"2025","journal-title":"Ann. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wenda, N., Fruth, K., Fisseler-Eckhoff, A., and Gosepath, J. (2023). The multifaceted role of confocal laser endomicroscopy in head and neck surgery: Oncologic and functional insights. Diagnostics, 13.","DOI":"10.3390\/diagnostics13193081"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ruan, W., Yang, J., Hou, Y., Yv, A., Ding, Q., and Zhou, Y. (2025). Clinical application of confocal laser endomicroscopy in the diagnosis of gastrointestinal diseases. Digestion.","DOI":"10.1159\/000548414"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.31557\/APJCP.2023.24.6.1949","article-title":"Role of confocal laser endomicroscopy in early detection of upper gastrointestinal malignancy in high risk patients","volume":"24","author":"Seerani","year":"2023","journal-title":"Asian Pac. J. Cancer Prev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1038\/s41698-024-00621-x","article-title":"Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy","volume":"8","author":"Cho","year":"2024","journal-title":"npj Precis. Oncol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, G., Li, G., Li, Z., Shao, X., Ji, R., Ma, T., Zhang, Y., Su, J., Qi, Q., and Guo, J. (2025). Deep learning-aided optical biopsy achieves whole-chain diagnosis of correa cascade of gastric cancer: A prospective study. BMC Med., 23.","DOI":"10.1186\/s12916-025-04310-9"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1053\/j.gastro.2011.08.007","article-title":"Magnifying narrowband imaging is more accurate than conventional white-light imaging in diagnosis of gastric mucosal cancer","volume":"141","author":"Ezoe","year":"2011","journal-title":"Gastroenterology"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.gie.2013.07.008","article-title":"An efficient diagnostic strategy for small, depressed early gastric cancer with magnifying narrow-band imaging: A post-hoc analysis of a prospective randomized controlled trial","volume":"79","author":"Yamada","year":"2014","journal-title":"Gastrointest. Endosc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.gii.2014.02.005","article-title":"Early detection of early gastric cancer using image-enhanced endoscopy: Current trends","volume":"3","author":"Song","year":"2014","journal-title":"Gastrointest. Interv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104026","DOI":"10.1016\/j.compbiomed.2020.104026","article-title":"Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images","volume":"126","author":"Yan","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1016\/j.gie.2020.11.014","article-title":"Identifying early gastric cancer under magnifying narrow-band images with deep learning: A multicenter study","volume":"93","author":"Hu","year":"2021","journal-title":"Gastrointest. Endosc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1111\/jgh.15190","article-title":"Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging","volume":"36","author":"Ueyama","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1111\/den.13688","article-title":"Detecting early gastric cancer: Comparison between the diagnostic ability of convolutional neural networks and endoscopists","volume":"33","author":"Ikenoyama","year":"2021","journal-title":"Dig. Endosc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1111\/den.13317","article-title":"Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective","volume":"31","author":"Mori","year":"2019","journal-title":"Dig. Endosc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","article-title":"Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images","volume":"21","author":"Hirasawa","year":"2018","journal-title":"Gastric Cancer"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yoon, H.J., Kim, S., Kim, J.-H., Keum, J.-S., Oh, S.-I., Jo, J., Chun, J., Youn, Y.H., Park, H., and Kwon, I.G. (2019). A lesion-based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J. Clin. Med., 8.","DOI":"10.3390\/jcm8091310"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ma, M., Li, Z., Yu, T., Liu, G., Ji, R., Li, G., Guo, Z., Wang, L., Qi, Q., and Yang, X. (2022). Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.945904"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1016\/j.gie.2021.03.013","article-title":"Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: A multicenter, diagnostic study (with video)","volume":"94","author":"Xu","year":"2021","journal-title":"Gastrointest. Endosc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.gie.2021.09.017","article-title":"Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos)","volume":"95","author":"Wu","year":"2022","journal-title":"Gastrointest. Endosc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103146","DOI":"10.1016\/j.ebiom.2020.103146","article-title":"Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study","volume":"62","author":"Tang","year":"2020","journal-title":"eBioMedicine"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10120-022-01358-x","article-title":"A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video)","volume":"26","author":"Du","year":"2023","journal-title":"Gastric Cancer"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1007\/s00535-009-0137-4","article-title":"Risk factors for complications of endoscopic submucosal dissection in gastric tumors: Analysis of 478 lesions","volume":"45","author":"Mannen","year":"2010","journal-title":"J. Gastroenterol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1111\/j.1443-1661.2010.01008.x","article-title":"Stricture after endoscopic submucosal dissection for early gastric cancers and adenomas","volume":"22","author":"Iizuka","year":"2010","journal-title":"Dig. Endosc."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1055\/s-2006-944716","article-title":"Endoscopic submucosal dissection for early gastric cancer: Results and degrees of technical difficulty as well as success","volume":"38","author":"Imagawa","year":"2006","journal-title":"Endoscopy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1055\/s-0033-1344396","article-title":"Short- and long-term outcomes of endoscopic submucosal dissection for undifferentiated early gastric cancer","volume":"45","author":"Abe","year":"2013","journal-title":"Endoscopy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10120-012-0163-4","article-title":"Gastric cancer treated in 2002 in japan: 2009 annual report of the JGCA nationwide registry","volume":"16","author":"Nashimoto","year":"2013","journal-title":"Gastric Cancer"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1055\/s-0044-100790","article-title":"Delineation of the extent of early gastric cancer by magnifying narrow-band imaging and chromoendoscopy: A multicenter randomized controlled trial","volume":"50","author":"Nagahama","year":"2018","journal-title":"Endoscopy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1007\/s10120-020-01071-7","article-title":"A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy","volume":"23","author":"An","year":"2020","journal-title":"Gastric Cancer"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s10120-022-01299-5","article-title":"A comparative study of demarcation line diagnostic performance between non-magnifying observation with white light and non-magnifying observation with narrow-band light for early gastric cancer","volume":"25","author":"Kojima","year":"2022","journal-title":"Gastric Cancer"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1080\/00365521.2025.2509818","article-title":"Artificial intelligence for early gastric cancer boundary recognition in NBI and nF-NBI endoscopic images","volume":"60","author":"Hong","year":"2025","journal-title":"Scand. J. Gastroenterol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Okumura, S., Goudo, M., Hiwa, S., Yasuda, T., Kitae, H., Yasuda, Y., Tomie, A., Omatsu, T., Ichikawa, H., and Yagi, N. (2022). Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102491"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1055\/a-1229-0920","article-title":"A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy","volume":"53","author":"Ling","year":"2021","journal-title":"Endoscopy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","article-title":"Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy","volume":"89","author":"Zhu","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1055\/a-0855-3532","article-title":"A deep neural network improves endoscopic detection of early gastric cancer without blind spots","volume":"51","author":"Wu","year":"2019","journal-title":"Endoscopy"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1136\/gutjnl-2018-317366","article-title":"Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy","volume":"68","author":"Wu","year":"2019","journal-title":"Gut"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.gie.2020.06.047","article-title":"Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer: Efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging","volume":"92","author":"Nagao","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.dld.2020.11.017","article-title":"Intelligent detection endoscopic assistant: An artificial intelligence-based system for monitoring blind spots during esophagogastroduodenoscopy in real-time","volume":"53","author":"Li","year":"2021","journal-title":"Dig. Liver Dis."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kim, J.-H., Oh, S.-I., Han, S.-Y., Keum, J.-S., Kim, K.-N., Chun, J.-Y., Youn, Y.-H., and Park, H. (2022). An optimal artificial intelligence system for real-time endoscopic prediction of invasion depth in early gastric cancer. Cancers, 14.","DOI":"10.3390\/cancers14236000"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.gie.2021.06.033","article-title":"Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos)","volume":"95","author":"Wu","year":"2022","journal-title":"Gastrointest. Endosc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1007\/s10120-022-01330-9","article-title":"Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer","volume":"26","author":"Goto","year":"2023","journal-title":"Gastric Cancer"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s00535-024-02102-1","article-title":"A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer","volume":"59","author":"Uema","year":"2024","journal-title":"J. Gastroenterol."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Japanese Gastric Cancer Association (2023). Japanese gastric cancer treatment guidelines 2021 (6th edition). Gastric Cancer, 26, 1\u201325.","DOI":"10.1007\/s10120-022-01331-8"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1055\/s-0030-1255617","article-title":"Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer","volume":"42","author":"Choi","year":"2010","journal-title":"Endoscopy"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1055\/a-2674-4912","article-title":"Performance measures for upper gastrointestinal endoscopy: A european society of gastrointestinal endoscopy (ESGE) quality improvement initiative\u2014Update 2025","volume":"57","author":"Areia","year":"2025","journal-title":"Endoscopy"},{"key":"ref_64","first-page":"11","article-title":"The endoscopic diagnosis of early gastric cancer","volume":"26","author":"Yao","year":"2013","journal-title":"Ann. Gastroenterol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1055\/s-2000-649","article-title":"Quality assurance in gastrointestinal endoscopy","volume":"32","author":"Naylor","year":"2000","journal-title":"Endoscopy"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1756284820916693","DOI":"10.1177\/1756284820916693","article-title":"Quality indicators in diagnostic upper gastrointestinal endoscopy","volume":"13","author":"Januszewicz","year":"2020","journal-title":"Ther. Adv. Gastroenterol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10120-016-0680-7","article-title":"Development of an e-learning system for teaching endoscopists how to diagnose early gastric cancer: Basic principles for improving early detection","volume":"20","author":"Yao","year":"2017","journal-title":"Gastric Cancer"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"827","DOI":"10.1016\/j.gie.2017.10.040","article-title":"Impact of experience on self-assessment accuracy of clinical colonoscopy competence","volume":"87","author":"Scaffidi","year":"2018","journal-title":"Gastrointest. Endosc."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1016\/j.gie.2021.12.019","article-title":"Correlation of the detection rate of upper GI cancer with artificial intelligence score: Results from a multicenter trial (with video)","volume":"95","author":"Li","year":"2022","journal-title":"Gastrointest. Endosc."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1053\/j.gastro.2022.03.053","article-title":"Comprehensive Diagnostic Performance of Real-Time Characterization of Colorectal Lesions Using an Artificial Intelligence-Assisted System: A Prospective Study","volume":"163","author":"Minegishi","year":"2022","journal-title":"Gastroenterology"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1136\/gutjnl-2018-317500","article-title":"Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: A prospective randomised controlled study","volume":"68","author":"Wang","year":"2019","journal-title":"Gut"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S2468-1253(19)30413-3","article-title":"Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): A randomised controlled study","volume":"5","author":"Gong","year":"2020","journal-title":"Lancet Gastroenterol. Hepatol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1055\/a-1556-5984","article-title":"Diagnostic accuracy of a novel artificial intelligence system for adenoma detection in daily practice: A prospective nonrandomized comparative study","volume":"54","author":"Zippelius","year":"2022","journal-title":"Endoscopy"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1055\/a-2189-7036","article-title":"A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: Development and multicenter prospective validation","volume":"56","author":"Wang","year":"2024","journal-title":"Endoscopy"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1053\/j.gastro.2022.03.007","article-title":"Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia","volume":"163","author":"Wallace","year":"2022","journal-title":"Gastroenterology"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1016\/j.cgh.2021.09.009","article-title":"Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial)","volume":"20","author":"Mansour","year":"2022","journal-title":"Clin. Gastroenterol. Hepatol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/14\/2196\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:13:49Z","timestamp":1784078029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/14\/2196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,14]]},"references-count":76,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["diagnostics16142196"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16142196","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,14]]}}}