{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T05:03:14Z","timestamp":1780549394549,"version":"3.54.1"},"reference-count":176,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Amethyst Healthcare Group"},{"name":"NIHR Academic Clinical Fellowship"},{"name":"Cleveland Clinic London PhD Fellowship"},{"name":"Department of Science, Innovation and Technology"},{"name":"Royal Academy of Engineering under the Chair in Emerging Technologies programme"},{"name":"NIHR Biomedical Research Centre at University College London"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-026-02401-2","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:14:34Z","timestamp":1772759674000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis"],"prefix":"10.1038","volume":"9","author":[{"given":"Amir","family":"Rafati Fard","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon C.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kieran J.","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasneet K.","family":"Dhaliwal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomas","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrito","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joachim","family":"Starup-Hansen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John G.","family":"Hanrahan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan Hee","family":"Koh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyal Z.","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani J.","family":"Marcus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"2401_CR1","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.1016\/S0140-6736(15)60806-6","volume":"385","author":"TG Weiser","year":"2015","unstructured":"Weiser, T. G. et al. Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes. Lancet 385, S11 (2015).","journal-title":"Lancet"},{"key":"2401_CR2","doi-asserted-by":"publisher","first-page":"102545","DOI":"10.1016\/j.eclinm.2024.102545","volume":"70","author":"A Yiu","year":"2024","unstructured":"Yiu, A., Lam, K., Simister, C., Clarke, J. & Kinross, J. Adoption of routine surgical video recording: a nationwide freedom of information act request across England and Wales. EClinicalMedicine 70, 102545 (2024).","journal-title":"EClinicalMedicine"},{"key":"2401_CR3","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1038\/s41746-022-00707-5","volume":"5","author":"P Mascagni","year":"2022","unstructured":"Mascagni, P. et al. Computer vision in surgery: from potential to clinical value. npj Digit. Med. 5, 163 (2022).","journal-title":"npj Digit. Med."},{"key":"2401_CR4","doi-asserted-by":"publisher","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","volume":"1","author":"X Liu","year":"2019","unstructured":"Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1, e271\u2013e297 (2019).","journal-title":"Lancet Digit. Health"},{"key":"2401_CR5","doi-asserted-by":"publisher","first-page":"m689","DOI":"10.1136\/bmj.m689","volume":"368","author":"M Nagendran","year":"2020","unstructured":"Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 368, m689 (2020).","journal-title":"BMJ"},{"key":"2401_CR6","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1038\/s41591-023-02732-7","volume":"30","author":"HJ Marcus","year":"2024","unstructured":"Marcus, H. J. et al. The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring. Nat. Med. 30, 61\u201375 (2024).","journal-title":"Nat. Med."},{"key":"2401_CR7","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1067\/mge.2001.118644","volume":"54","author":"ID Norton","year":"2001","unstructured":"Norton, I. D. et al. Neural network analysis of EUS images to differentiate between pancreatic malignancy and pancreatitis. Gastrointest. Endosc. 54, 625\u2013629 (2001).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR8","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.cmpb.2010.01.004","volume":"99","author":"D Voigt","year":"2010","unstructured":"Voigt, D., D\u00f6llinger, M., Yang, A., Eysholdt, U. & Lohscheller, J. Automatic diagnosis of vocal fold paresis by employing phonovibrogram features and machine learning methods. Comput. Methods Prog. Biomed. 99, 275\u2013288 (2010).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"2401_CR9","doi-asserted-by":"publisher","first-page":"5560","DOI":"10.3748\/wjg.v18.i39.5560","volume":"18","author":"B Andr\u00e9","year":"2012","unstructured":"Andr\u00e9, B. et al. Software for automated classification of probe-based confocal laser endomicroscopy videos of colorectal polyps. World J. Gastroenterol. 18, 5560\u20135569 (2012).","journal-title":"World J. Gastroenterol."},{"key":"2401_CR10","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.gie.2011.08.051","volume":"75","author":"Y Takemura","year":"2012","unstructured":"Takemura, Y. et al. Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video). Gastrointest. Endosc. 75, 179\u2013185 (2012).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR11","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1055\/s-0042-113609","volume":"48","author":"Y Mori","year":"2016","unstructured":"Mori, Y. et al. Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study. Endoscopy 48, 1110\u20131118 (2016).","journal-title":"Endoscopy"},{"key":"2401_CR12","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1053\/j.gastro.2018.06.037","volume":"155","author":"G Urban","year":"2018","unstructured":"Urban, G. et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 155, 1069\u20131078.e8 (2018).","journal-title":"Gastroenterology"},{"key":"2401_CR13","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.gie.2019.06.044","volume":"90","author":"S-L Cai","year":"2019","unstructured":"Cai, S.-L. et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest. Endosc. 90, 745\u2013753.e2 (2019).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR14","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s11548-018-1886-4","volume":"14","author":"P Sadda","year":"2019","unstructured":"Sadda, P. et al. Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery. Int. J. Comput. Assist. Radio. Surg. 14, 227\u2013235 (2019).","journal-title":"Int. J. Comput. Assist. Radio. Surg."},{"key":"2401_CR15","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1055\/a-0855-3532","volume":"51","author":"L Wu","year":"2019","unstructured":"Wu, L. et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy 51, 522\u2013531 (2019).","journal-title":"Endoscopy"},{"key":"2401_CR16","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.1136\/gutjnl-2018-317366","volume":"68","author":"L Wu","year":"2019","unstructured":"Wu, L. et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut 68, 2161\u20132169 (2019).","journal-title":"Gut"},{"key":"2401_CR17","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1097\/MCG.0000000000001272","volume":"54","author":"A Becq","year":"2020","unstructured":"Becq, A. et al. Effectiveness of a deep-learning polyp detection system in prospectively collected colonoscopy videos with variable bowel preparation quality. J. Clin. Gastroenterol. 54, 554\u2013557 (2020).","journal-title":"J. Clin. Gastroenterol."},{"key":"2401_CR18","doi-asserted-by":"crossref","unstructured":"Cincar, K. & Sima, I. Machine learning algorithms approach for gastrointestinal polyps classification. In Proc. International Conference on INnovations in Intelligent SysTems and Applications (INISTA) 1\u20136 (2020).","DOI":"10.1109\/INISTA49547.2020.9194659"},{"key":"2401_CR19","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1016\/j.gie.2020.05.043","volume":"92","author":"H Fukuda","year":"2020","unstructured":"Fukuda, H. et al. Comparison of performances of artificial intelligence versus expert endoscopists for real-time assisted diagnosis of esophageal squamous cell carcinoma (with video). Gastrointest. Endosc. 92, 848\u2013855 (2020).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR20","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s11548-020-02127-w","volume":"15","author":"LC Garc\u00eda-Peraza-Herrera","year":"2020","unstructured":"Garc\u00eda-Peraza-Herrera, L. C. et al. Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology. Int. J. Comput. Assist. Radio. Surg. 15, 651\u2013659 (2020).","journal-title":"Int. J. Comput. Assist. Radio. Surg."},{"key":"2401_CR21","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1016\/j.gie.2020.04.079","volume":"92","author":"Y Horiuchi","year":"2020","unstructured":"Horiuchi, Y. et al. Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging (with videos). Gastrointest. Endosc. 92, 856\u2013865.e1 (2020).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR22","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s11548-019-02108-8","volume":"15","author":"G Lecuyer","year":"2020","unstructured":"Lecuyer, G., Ragot, M., Martin, N., Launay, L. & Jannin, P. Assisted phase and step annotation for surgical videos. Int. J. Comput. Assist. Radio. Surg. 15, 673\u2013680 (2020).","journal-title":"Int. J. Comput. Assist. Radio. Surg."},{"key":"2401_CR23","doi-asserted-by":"publisher","first-page":"3403","DOI":"10.1109\/TMI.2020.2994778","volume":"39","author":"Y Li","year":"2020","unstructured":"Li, Y. et al. Computer-aided cervical cancer diagnosis using time-lapsed colposcopic images. IEEE Trans. Med. Imaging 39, 3403\u20133415 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2401_CR24","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1007\/s10120-020-01077-1","volume":"23","author":"H Nakashima","year":"2020","unstructured":"Nakashima, H., Kawahira, H., Kawachi, H. & Sakaki, N. Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video). Gastric Cancer 23, 1033\u20131040 (2020).","journal-title":"Gastric Cancer"},{"key":"2401_CR25","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1007\/s00535-020-01716-5","volume":"55","author":"Y Shimamoto","year":"2020","unstructured":"Shimamoto, Y. et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J. Gastroenterol. 55, 1037\u20131045 (2020).","journal-title":"J. Gastroenterol."},{"key":"2401_CR26","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.gie.2019.08.026","volume":"91","author":"J-R Su","year":"2020","unstructured":"Su, J.-R. et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest. Endosc. 91, 415\u2013424.e4 (2020).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.103146","volume":"62","author":"D Tang","year":"2020","unstructured":"Tang, D. et al. Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: a multicentre retrospective diagnostic study. EBioMedicine 62, 103146 (2020).","journal-title":"EBioMedicine"},{"key":"2401_CR28","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1053\/j.gastro.2020.06.023","volume":"159","author":"P Wang","year":"2020","unstructured":"Wang, P. et al. Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study. Gastroenterology 159, 1252\u20131261.e5 (2020).","journal-title":"Gastroenterology"},{"key":"2401_CR29","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-020-01860-y","volume":"18","author":"P Xue","year":"2020","unstructured":"Xue, P. et al. Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies. BMC Med. 18, 406 (2020).","journal-title":"BMC Med."},{"key":"2401_CR30","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.gie.2019.11.026","volume":"91","author":"J Zhou","year":"2020","unstructured":"Zhou, J. et al. A novel artificial intelligence system for the assessment of bowel preparation (with video). Gastrointest. Endosc. 91, 428\u2013435.e2 (2020).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR31","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.gie.2020.06.058","volume":"93","author":"M Chen","year":"2021","unstructured":"Chen, M. et al. Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using a deep convolutional neural network: a multicenter retrospective study (with video). Gastrointest. Endosc. 93, 422\u2013432.e3 (2021).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR32","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1002\/mp.14822","volume":"48","author":"P Cui","year":"2021","unstructured":"Cui, P., Shu, T., Lei, J. & Chen, W. Nerve recognition in percutaneous transforaminal endoscopic discectomy using convolutional neural network. Med. Phys. 48, 2279\u20132288 (2021).","journal-title":"Med. Phys."},{"key":"2401_CR33","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.gie.2021.01.043","volume":"94","author":"MA Everson","year":"2021","unstructured":"Everson, M. A. et al. A clinically interpretable convolutional neural network for the real-time prediction of early squamous cell cancer of the esophagus: comparing diagnostic performance with a panel of expert European and Asian endoscopists. Gastrointest. Endosc. 94, 273\u2013281 (2021).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR34","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1093\/pcmedi\/pbab013","volume":"4","author":"J Jiang","year":"2021","unstructured":"Jiang, J. et al. AI based colorectal disease detection using real-time screening colonoscopy. Precis Clin. Med. 4, 109\u2013118 (2021).","journal-title":"Precis Clin. Med."},{"key":"2401_CR35","doi-asserted-by":"publisher","first-page":"281","DOI":"10.3748\/wjg.v27.i3.281","volume":"27","author":"B Li","year":"2021","unstructured":"Li, B. et al. Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging. World J. Gastroenterol. 27, 281\u2013293 (2021).","journal-title":"World J. Gastroenterol."},{"key":"2401_CR36","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1055\/a-1229-0920","volume":"53","author":"T Ling","year":"2021","unstructured":"Ling, T. et al. A deep learning-based system for identifying differentiation status and delineating the margins of early gastric cancer in magnifying narrow-band imaging endoscopy. Endoscopy 53, 469\u2013477 (2021).","journal-title":"Endoscopy"},{"key":"2401_CR37","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1016\/j.gie.2021.06.021","volume":"94","author":"DM Livovsky","year":"2021","unstructured":"Livovsky, D. M. et al. Detection of elusive polyps using a large-scale artificial intelligence system (with videos). Gastrointest. Endosc. 94, 1099\u20131109.e10 (2021).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-87405-6","volume":"11","author":"S Shiroma","year":"2021","unstructured":"Shiroma, S. et al. Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance. Sci. Rep. 11, 7759 (2021).","journal-title":"Sci. Rep."},{"key":"2401_CR39","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.622827","volume":"11","author":"D Tang","year":"2021","unstructured":"Tang, D. et al. A novel model based on deep convolutional neural network improves diagnostic accuracy of intramucosal gastric cancer (with video). Front. Oncol. 11, 622827 (2021).","journal-title":"Front. Oncol."},{"key":"2401_CR40","doi-asserted-by":"publisher","first-page":"1101","DOI":"10.1111\/den.13934","volume":"33","author":"K Waki","year":"2021","unstructured":"Waki, K. et al. Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation. Dig. Endosc. 33, 1101\u20131109 (2021).","journal-title":"Dig. Endosc."},{"key":"2401_CR41","first-page":"1735","volume":"43","author":"H Wu","year":"2021","unstructured":"Wu, H. et al. Application of artificial intelligence technology based on convolutional neural network in the identification of early gastric cancer. J. Third Mil. Med. Univ. 43, 1735\u20131742 (2021).","journal-title":"J. Third Mil. Med. Univ."},{"key":"2401_CR42","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1016\/j.gie.2021.03.013","volume":"94","author":"M Xu","year":"2021","unstructured":"Xu, M. et al. Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video). Gastrointest. Endosc. 94, 540\u2013548.e4 (2021).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR43","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1111\/den.13908","volume":"33","author":"X-X Yang","year":"2021","unstructured":"Yang, X.-X. et al. Real-time artificial intelligence for endoscopic diagnosis of early esophageal squamous cell cancer (with video). Dig. Endosc. 33, 1075\u20131084 (2021).","journal-title":"Dig. Endosc."},{"key":"2401_CR44","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-03219-6","volume":"11","author":"JY Yoo","year":"2021","unstructured":"Yoo, J. Y. et al. Deep learning for anatomical interpretation of video bronchoscopy images. Sci. Rep. 11, 23765 (2021).","journal-title":"Sci. Rep."},{"key":"2401_CR45","doi-asserted-by":"publisher","first-page":"5232","DOI":"10.3748\/wjg.v27.i31.5232","volume":"27","author":"S-B Zhao","year":"2021","unstructured":"Zhao, S.-B. et al. Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning. World J. Gastroenterol. 27, 5232\u20135246 (2021).","journal-title":"World J. Gastroenterol."},{"key":"2401_CR46","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.673775","volume":"11","author":"X Zhi","year":"2021","unstructured":"Zhi, X. et al. Automatic image selection model based on machine learning for endobronchial ultrasound strain elastography videos. Front. Oncol. 11, 673775 (2021).","journal-title":"Front. Oncol."},{"key":"2401_CR47","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1080\/00365521.2022.2070436","volume":"57","author":"D Ainechi","year":"2022","unstructured":"Ainechi, D. et al. Impact of artificial intelligence on colorectal polyp detection for early-career endoscopists: an international comparative study. Scand. J. Gastroenterol. 57, 1272\u20131277 (2022).","journal-title":"Scand. J. Gastroenterol."},{"key":"2401_CR48","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1038\/s41746-022-00633-6","volume":"5","author":"C Biffi","year":"2022","unstructured":"Biffi, C. et al. A novel AI device for real-time optical characterization of colorectal polyps. npj Digit Med 5, 84 (2022).","journal-title":"npj Digit Med"},{"key":"2401_CR49","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.gie.2021.11.040","volume":"95","author":"X He","year":"2022","unstructured":"He, X. et al. Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos). Gastrointest. Endosc. 95, 671\u2013678.e4 (2022).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR50","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1002\/ueg2.12233","volume":"10","author":"M Hussein","year":"2022","unstructured":"Hussein, M. et al. A new artificial intelligence system successfully detects and localises early neoplasia in Barrett\u2019s esophagus by using convolutional neural networks. U. Eur. Gastroenterol. J. 10, 528\u2013537 (2022).","journal-title":"U. Eur. Gastroenterol. J."},{"key":"2401_CR51","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.21037\/tlcr-22-761","volume":"11","author":"Y Li","year":"2022","unstructured":"Li, Y. et al. Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification. Transl. Lung Cancer Res. 11, 2261\u20132274 (2022).","journal-title":"Transl. Lung Cancer Res."},{"key":"2401_CR52","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.1016\/j.gie.2021.11.049","volume":"95","author":"Z Lu","year":"2022","unstructured":"Lu, Z. et al. Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video). Gastrointest. Endosc. 95, 1186\u20131194.e3 (2022).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR53","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.945904","volume":"12","author":"M Ma","year":"2022","unstructured":"Ma, M. et al. Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front. Oncol. 12, 945904 (2022).","journal-title":"Front. Oncol."},{"key":"2401_CR54","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-11549-2","volume":"12","author":"DJ Pangal","year":"2022","unstructured":"Pangal, D. J. et al. Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1\u2009min of video. Sci. Rep. 12, 8137 (2022).","journal-title":"Sci. Rep."},{"key":"2401_CR55","doi-asserted-by":"publisher","first-page":"5531","DOI":"10.1007\/s00464-022-09268-w","volume":"36","author":"K Sato","year":"2022","unstructured":"Sato, K. et al. Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence. Surg. Endosc. 36, 5531\u20135539 (2022).","journal-title":"Surg. Endosc."},{"key":"2401_CR56","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-10739-2","volume":"12","author":"A Tajiri","year":"2022","unstructured":"Tajiri, A. et al. Utility of an artificial intelligence system for classification of esophageal lesions when simulating its clinical use. Sci. Rep. 12, 6677 (2022).","journal-title":"Sci. Rep."},{"key":"2401_CR57","doi-asserted-by":"publisher","first-page":"7800","DOI":"10.1007\/s00464-022-09319-2","volume":"36","author":"D Tang","year":"2022","unstructured":"Tang, D. et al. A deep learning-based model improves diagnosis of early gastric cancer under narrow band imaging endoscopy. Surg. Endosc. 36, 7800\u20137810 (2022).","journal-title":"Surg. Endosc."},{"key":"2401_CR58","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1016\/j.gie.2021.12.020","volume":"95","author":"C-P Tang","year":"2022","unstructured":"Tang, C.-P. et al. Polyp detection and false-positive rates by computer-aided analysis of withdrawal-phase videos of colonoscopy of the right-sided colon segment in a randomized controlled trial comparing water exchange and air insufflation. Gastrointest. Endosc. 95, 1198\u20131206.e6 (2022).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijsu.2022.106740","volume":"104","author":"T Wang","year":"2022","unstructured":"Wang, T. et al. Intelligent cataract surgery supervision and evaluation via deep learning. Int. J. Surg. 104, 106740 (2022).","journal-title":"Int. J. Surg."},{"key":"2401_CR60","doi-asserted-by":"publisher","first-page":"2516","DOI":"10.1002\/lary.30173","volume":"132","author":"B Wang","year":"2022","unstructured":"Wang, B. et al. Development of artificial intelligence for parathyroid recognition during endoscopic thyroid surgery. Laryngoscope 132, 2516\u20132523 (2022).","journal-title":"Laryngoscope"},{"key":"2401_CR61","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.gie.2021.09.017","volume":"95","author":"L Wu","year":"2022","unstructured":"Wu, L. et al. Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest. Endosc. 95, 269\u2013280.e6 (2022).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR62","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.gie.2021.06.033","volume":"95","author":"L Wu","year":"2022","unstructured":"Wu, L. et al. Deep learning system compared with expert endoscopists in predicting early gastric cancer and its invasion depth and differentiation status (with videos). Gastrointest. Endosc. 95, 92\u2013104.e3 (2022).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR63","doi-asserted-by":"publisher","first-page":"e2221992","DOI":"10.1001\/jamanetworkopen.2022.21992","volume":"5","author":"X Xie","year":"2022","unstructured":"Xie, X. et al. Development and validation of an artificial intelligence model for small bowel capsule endoscopy video review. JAMA Netw. Open 5, e2221992 (2022).","journal-title":"JAMA Netw. Open"},{"key":"2401_CR64","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1111\/jgh.15689","volume":"37","author":"X-L Yuan","year":"2022","unstructured":"Yuan, X.-L. et al. Artificial intelligence for detecting superficial esophageal squamous cell carcinoma under multiple endoscopic imaging modalities: a multicenter study. J. Gastroenterol. Hepatol. 37, 169\u2013178 (2022).","journal-title":"J. Gastroenterol. Hepatol."},{"key":"2401_CR65","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1186\/s12876-022-02427-2","volume":"22","author":"Q Zhao","year":"2022","unstructured":"Zhao, Q., Jia, Q. & Chi, T. Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study. BMC Gastroenterol. 22, 352 (2022).","journal-title":"BMC Gastroenterol."},{"key":"2401_CR66","doi-asserted-by":"publisher","DOI":"10.1186\/s12876-022-02212-1","volume":"22","author":"Q Zhao","year":"2022","unstructured":"Zhao, Q. & Chi, T. Deep learning model can improve the diagnosis rate of endoscopic chronic atrophic gastritis: a prospective cohort study. BMC Gastroenterol. 22, 133 (2022).","journal-title":"BMC Gastroenterol."},{"key":"2401_CR67","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.gie.2022.10.031","volume":"97","author":"M Abdelrahim","year":"2023","unstructured":"Abdelrahim, M. et al. Development and validation of artificial neural networks model for detection of Barrett\u2019s neoplasia: a multicenter pragmatic nonrandomized trial (with video). Gastrointest. Endosc. 97, 422\u2013434 (2023).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR68","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1055\/a-1881-4209","volume":"55","author":"Z Ding","year":"2023","unstructured":"Ding, Z. et al. Artificial intelligence-based diagnosis of abnormalities in small-bowel capsule endoscopy. Endoscopy 55, 44\u201351 (2023).","journal-title":"Endoscopy"},{"key":"2401_CR69","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1007\/s00535-023-02025-3","volume":"58","author":"Z Dong","year":"2023","unstructured":"Dong, Z. et al. Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions. J. Gastroenterol. 58, 978\u2013989 (2023).","journal-title":"J. Gastroenterol."},{"key":"2401_CR70","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1038\/s41746-023-00813-y","volume":"6","author":"Z Dong","year":"2023","unstructured":"Dong, Z. et al. Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy. npj Digit. Med. 6, 64 (2023).","journal-title":"npj Digit. Med."},{"key":"2401_CR71","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s10120-022-01358-x","volume":"26","author":"H Du","year":"2023","unstructured":"Du, H. et al. A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video). Gastric Cancer 26, 275\u2013285 (2023).","journal-title":"Gastric Cancer"},{"key":"2401_CR72","doi-asserted-by":"publisher","first-page":"100799","DOI":"10.1016\/j.irbm.2023.100799","volume":"44","author":"S Fang","year":"2023","unstructured":"Fang, S., Fu, J., Du, C., Lin, T. & Yan, Y. Identifying laryngeal neoplasms in laryngoscope images via deep learning based object detection: a case study on an extremely small data set. IRBM 44, 100799 (2023).","journal-title":"IRBM"},{"key":"2401_CR73","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1296249","volume":"10","author":"L Feng","year":"2023","unstructured":"Feng, L. et al. Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video. Front. Med. 10, 1296249 (2023).","journal-title":"Front. Med."},{"key":"2401_CR74","doi-asserted-by":"publisher","first-page":"5665","DOI":"10.1007\/s00464-023-09870-6","volume":"37","author":"D Fer","year":"2023","unstructured":"Fer, D. et al. An artificial intelligence model that automatically labels Roux-en-Y gastric bypasses, a comparison to trained surgeon annotators. Surg. Endosc. 37, 5665\u20135672 (2023).","journal-title":"Surg. Endosc."},{"key":"2401_CR75","doi-asserted-by":"publisher","first-page":"e905","DOI":"10.1016\/S2589-7500(23)00199-1","volume":"5","author":"KN Fockens","year":"2023","unstructured":"Fockens, K. N. et al. A deep learning system for detection of early Barrett\u2019s neoplasia: a model development and validation study. Lancet Digit. Health 5, e905\u2013e916 (2023).","journal-title":"Lancet Digit. Health"},{"key":"2401_CR76","doi-asserted-by":"publisher","DOI":"10.1002\/deo2.178","volume":"3","author":"E Hossain","year":"2023","unstructured":"Hossain, E. et al. Performance of a novel computer-aided diagnosis system in the characterization of colorectal polyps, and its role in meeting Preservation and Incorporation of Valuable Endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy. DEN Open 3, e178 (2023).","journal-title":"DEN Open"},{"key":"2401_CR77","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s00464-022-09470-w","volume":"37","author":"FH Koh","year":"2023","unstructured":"Koh, F. H. et al. Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore. Surg. Endosc. 37, 165\u2013171 (2023).","journal-title":"Surg. Endosc."},{"key":"2401_CR78","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-023-04572-y","volume":"21","author":"Y Li","year":"2023","unstructured":"Li, Y. et al. Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data. J. Transl. Med. 21, 698 (2023).","journal-title":"J. Transl. Med."},{"key":"2401_CR79","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1016\/j.dld.2023.02.010","volume":"55","author":"Y-D Li","year":"2023","unstructured":"Li, Y.-D. et al. Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time. Dig. Liver Dis. 55, 649\u2013654 (2023).","journal-title":"Dig. Liver Dis."},{"key":"2401_CR80","doi-asserted-by":"publisher","first-page":"7472","DOI":"10.1245\/s10434-023-13862-0","volume":"30","author":"S Matsuda","year":"2023","unstructured":"Matsuda, S. et al. Endoscopic evaluation of pathological complete response using deep neural network in esophageal cancer patients who received neoadjuvant chemotherapy-multicenter retrospective study from four Japanese esophageal centers. Ann. Surg. Oncol. 30, 7472\u20137480 (2023).","journal-title":"Ann. Surg. Oncol."},{"key":"2401_CR81","doi-asserted-by":"publisher","first-page":"3733","DOI":"10.1245\/s10434-023-13140-z","volume":"30","author":"S Matsuda","year":"2023","unstructured":"Matsuda, S. et al. Evaluation of endoscopic response using deep neural network in esophageal cancer patients who received neoadjuvant chemotherapy. Ann. Surg. Oncol. 30, 3733\u20133742 (2023).","journal-title":"Ann. Surg. Oncol."},{"key":"2401_CR82","doi-asserted-by":"publisher","first-page":"4840","DOI":"10.3390\/jcm12144840","volume":"12","author":"Y Misumi","year":"2023","unstructured":"Misumi, Y. et al. Comparison of the ability of artificial-intelligence-based computer-aided detection (CAD) systems and endoscopists to detect colorectal neoplastic lesions on endoscopy video. J. Clin. Med. 12, 4840 (2023).","journal-title":"J. Clin. Med."},{"key":"2401_CR83","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.gie.2023.02.016","volume":"98","author":"F Nehme","year":"2023","unstructured":"Nehme, F. et al. Performance and attitudes toward real-time computer-aided polyp detection during colonoscopy in a large tertiary referral center in the United States. Gastrointest. Endosc. 98, 100\u2013109.e6 (2023).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR84","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.gie.2023.01.050","volume":"98","author":"D Nemoto","year":"2023","unstructured":"Nemoto, D. et al. Computer-aided diagnosis of early-stage colorectal cancer using nonmagnified endoscopic white-light images (with videos). Gastrointest. Endosc. 98, 90\u201399.e4 (2023).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR85","doi-asserted-by":"publisher","first-page":"E970","DOI":"10.1055\/a-2161-1816","volume":"11","author":"FJ O\u2019Hara","year":"2023","unstructured":"O\u2019Hara, F. J. & Mc Namara, D. Capsule endoscopy with artificial intelligence-assisted technology: real-world usage of a validated AI model for capsule image review. Endosc. Int. Open 11, E970\u2013E975 (2023).","journal-title":"Endosc. Int. Open"},{"key":"2401_CR86","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1111\/jgh.16274","volume":"38","author":"SXZ Quek","year":"2023","unstructured":"Quek, S. X. Z. et al. Comparing artificial intelligence to humans for endoscopic diagnosis of gastric neoplasia: an external validation study. J. Gastroenterol. Hepatol. 38, 1587\u20131591 (2023).","journal-title":"J. Gastroenterol. Hepatol."},{"key":"2401_CR87","doi-asserted-by":"crossref","unstructured":"Sedighipour Chafjiri, F., Mohebbian, M. R., Wahid, K. A. & Babyn, P. Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent features. Multimed. Tools Appl. 1\u201322 (2023).","DOI":"10.1007\/s11042-023-14982-1"},{"key":"2401_CR88","doi-asserted-by":"publisher","DOI":"10.14309\/ctg.0000000000000643","volume":"14","author":"Y Shen","year":"2023","unstructured":"Shen, Y. et al. Real-time evaluation of helicobacter pylori infection by convolution neural network during white-light endoscopy: a prospective, multicenter study (with video). Clin. Transl. Gastroenterol. 14, e00643 (2023).","journal-title":"Clin. Transl. Gastroenterol."},{"key":"2401_CR89","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1122247","volume":"13","author":"Y Shi","year":"2023","unstructured":"Shi, Y. et al. Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy. Front Oncol. 13, 1122247 (2023).","journal-title":"Front Oncol."},{"key":"2401_CR90","doi-asserted-by":"crossref","unstructured":"Smedsrud, P. H. et al. Man vs. AI: An in silico study of polyp detection performance. In Proc. IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) 720\u2013725 (2023).","DOI":"10.1109\/CBMS58004.2023.00307"},{"key":"2401_CR91","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1055\/a-2077-7398","volume":"55","author":"B Sudarevic","year":"2023","unstructured":"Sudarevic, B. et al. Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference. Endoscopy 55, 871\u2013876 (2023).","journal-title":"Endoscopy"},{"key":"2401_CR92","doi-asserted-by":"publisher","first-page":"102027","DOI":"10.1016\/j.eclinm.2023.102027","volume":"60","author":"Y-K Sun","year":"2023","unstructured":"Sun, Y.-K. et al. Three-dimensional convolutional neural network model to identify clinically significant prostate cancer in transrectal ultrasound videos: a prospective, multi-institutional, diagnostic study. EClinicalMedicine 60, 102027 (2023).","journal-title":"EClinicalMedicine"},{"key":"2401_CR93","doi-asserted-by":"crossref","unstructured":"Wang, Z. et al. Real-time auto-segmentation of the ureter in video sequences of gynaecological laparoscopic surgery. Int. J. Med. Robot e2604 (2023).","DOI":"10.1002\/rcs.2604"},{"key":"2401_CR94","doi-asserted-by":"publisher","first-page":"7780","DOI":"10.1109\/TII.2022.3208364","volume":"19","author":"D Wang","year":"2023","unstructured":"Wang, D., Wang, X., Wang, S. & Yin, Y. Explainable multitask shapley explanation networks for real-time polyp diagnosis in videos. IEEE Trans. Ind. Inform. 19, 7780\u20137789 (2023).","journal-title":"IEEE Trans. Ind. Inform."},{"key":"2401_CR95","doi-asserted-by":"publisher","first-page":"1841","DOI":"10.14309\/ajg.0000000000002239","volume":"118","author":"MT Wei","year":"2023","unstructured":"Wei, M. T. et al. Evaluation of computer-aided detection during colonoscopy in the community (AI-SEE): a multicenter randomized clinical trial. Am. J. Gastroenterol. 118, 1841\u20131847 (2023).","journal-title":"Am. J. Gastroenterol."},{"key":"2401_CR96","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/JBHI.2022.3152625","volume":"27","author":"M Xu","year":"2023","unstructured":"Xu, M. et al. Automatic representative frame selection and intrathoracic lymph node diagnosis with endobronchial ultrasound elastography videos. IEEE J. Biomed. Health Inf. 27, 29\u201340 (2023).","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"2401_CR97","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1111\/den.14493","volume":"35","author":"L Yao","year":"2023","unstructured":"Yao, L. et al. Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data. Digest. Endosc. 35, 625\u2013635 (2023).","journal-title":"Digest. Endosc."},{"key":"2401_CR98","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.gie.2022.12.003","volume":"97","author":"X-L Yuan","year":"2023","unstructured":"Yuan, X.-L. et al. Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video). Gastrointest. Endosc. 97, 664\u2013672.e4 (2023).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR99","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-38913-0","volume":"13","author":"R Yui","year":"2023","unstructured":"Yui, R. et al. Preoperative prediction of sinonasal papilloma by artificial intelligence using nasal video endoscopy: a retrospective study. Sci. Rep. 13, 12439 (2023).","journal-title":"Sci. Rep."},{"key":"2401_CR100","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.gie.2023.02.026","volume":"98","author":"X Zhang","year":"2023","unstructured":"Zhang, X. et al. A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos). Gastrointest. Endosc. 98, 199\u2013210.e10 (2023).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR101","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.gie.2023.02.025","volume":"98","author":"L Zhang","year":"2023","unstructured":"Zhang, L. et al. Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with video). Gastrointest. Endosc. 98, 181\u2013190.e10 (2023).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR102","doi-asserted-by":"publisher","first-page":"175628482312086","DOI":"10.1177\/17562848231208669","volume":"16","author":"Q Zhao","year":"2023","unstructured":"Zhao, Q., Jia, Q. & Chi, T. U-Net deep learning model for endoscopic diagnosis of chronic atrophic gastritis and operative link for gastritis assessment staging: a prospective nested case-control study. Ther. Adv. Gastroenterol. 16, 17562848231208669 (2023).","journal-title":"Ther. Adv. Gastroenterol."},{"key":"2401_CR103","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1007\/s11548-023-02871-9","volume":"18","author":"J Zhou","year":"2023","unstructured":"Zhou, J. et al. Shifted-windows transformers for the detection of cerebral aneurysms in microsurgery. Int. J. Comput Assist Radio. Surg. 18, 1033\u20131041 (2023).","journal-title":"Int. J. Comput Assist Radio. Surg."},{"key":"2401_CR104","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1186\/s12876-024-03482-7","volume":"24","author":"J Chen","year":"2024","unstructured":"Chen, J. et al. Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video). BMC Gastroenterol. 24, 394 (2024).","journal-title":"BMC Gastroenterol."},{"key":"2401_CR105","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1111\/den.14670","volume":"36","author":"KS Choi","year":"2024","unstructured":"Choi, K. S. et al. Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield. Digest. Endosc. 36, 437\u2013445 (2024).","journal-title":"Digest. Endosc."},{"key":"2401_CR106","doi-asserted-by":"publisher","first-page":"9344","DOI":"10.1245\/s10434-024-16157-0","volume":"31","author":"T Furube","year":"2024","unstructured":"Furube, T. et al. Usefulness of an artificial intelligence model in recognizing recurrent laryngeal nerves during robot-assisted minimally invasive esophagectomy. Ann. Surg. Oncol. 31, 9344\u20139351 (2024).","journal-title":"Ann. Surg. Oncol."},{"key":"2401_CR107","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/s00423-024-03364-2","volume":"409","author":"NP Hardy","year":"2024","unstructured":"Hardy, N. P. et al. Surgeon assessment of significant rectal polyps using white light endoscopy alone and in comparison to fluorescence-augmented AI lesion classification. Langenbecks Arch. Surg. 409, 170 (2024).","journal-title":"Langenbecks Arch. Surg."},{"key":"2401_CR108","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1055\/a-2252-4874","volume":"56","author":"X-J He","year":"2024","unstructured":"He, X.-J. et al. Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study. Endoscopy 56, 334\u2013342 (2024).","journal-title":"Endoscopy"},{"key":"2401_CR109","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s11548-024-03093-3","volume":"19","author":"H Hoffmann","year":"2024","unstructured":"Hoffmann, H. et al. AIxSuture: vision-based assessment of open suturing skills. Int. J. Comput Assist Radio. Surg. 19, 1045\u20131052 (2024).","journal-title":"Int. J. Comput Assist Radio. Surg."},{"key":"2401_CR110","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.gie.2024.04.013","volume":"100","author":"JB Jukema","year":"2024","unstructured":"Jukema, J. B. et al. Computer-aided diagnosis improves characterization of Barrett\u2019s neoplasia by general endoscopists (with video). Gastrointest. Endosc. 100, 616\u2013625.e8 (2024).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR111","doi-asserted-by":"publisher","first-page":"6083","DOI":"10.1007\/s00405-024-08801-y","volume":"281","author":"\u00d6T Kavak","year":"2024","unstructured":"Kavak, \u00d6T., G\u00fcnd\u00fcz, \u015e, Vural, C. & Enver, N. Artificial intelligence based diagnosis of sulcus: assesment of videostroboscopy via deep learning. Eur. Arch. Otorhinolaryngol. 281, 6083\u20136091 (2024).","journal-title":"Eur. Arch. Otorhinolaryngol."},{"key":"2401_CR112","doi-asserted-by":"publisher","first-page":"e797","DOI":"10.1016\/j.wneu.2024.07.219","volume":"190","author":"DZ Khan","year":"2024","unstructured":"Khan, D. Z. et al. Video-based performance analysis in pituitary surgery - part 2: artificial intelligence assisted surgical coaching. World Neurosurg. 190, e797\u2013e808 (2024).","journal-title":"World Neurosurg."},{"key":"2401_CR113","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1038\/s41746-024-01134-4","volume":"7","author":"\u00c9 Labrecque Langlais","year":"2024","unstructured":"Labrecque Langlais, \u00c9 et al. Evaluation of stenoses using AI video models applied to coronary angiography. npj Digit Med 7, 138 (2024).","journal-title":"npj Digit Med"},{"key":"2401_CR114","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1007\/s10120-024-01524-3","volume":"27","author":"S Lee","year":"2024","unstructured":"Lee, S. et al. An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video). Gastric Cancer 27, 1088\u20131099 (2024).","journal-title":"Gastric Cancer"},{"key":"2401_CR115","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1097\/MCG.0000000000001972","volume":"58","author":"Q Li","year":"2024","unstructured":"Li, Q. et al. A deep learning application of capsule endoscopic gastric structure recognition based on a transformer model. J. Clin. Gastroenterol. 58, 937\u2013943 (2024).","journal-title":"J. Clin. Gastroenterol."},{"key":"2401_CR116","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinre.2024.102334","volume":"48","author":"L Li","year":"2024","unstructured":"Li, L. et al. Automated detection of small bowel lesions based on capsule endoscopy using deep learning algorithm. Clin. Res. Hepatol. Gastroenterol. 48, 102334 (2024).","journal-title":"Clin. Res. Hepatol. Gastroenterol."},{"key":"2401_CR117","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1053\/j.gastro.2024.03.039","volume":"167","author":"X Luo","year":"2024","unstructured":"Luo, X. et al. Rapid endoscopic diagnosis of benign ulcerative colorectal diseases with an artificial intelligence contextual framework. Gastroenterology 167, 591\u2013603.e9 (2024).","journal-title":"Gastroenterology"},{"key":"2401_CR118","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1055\/a-2296-5696","volume":"56","author":"M Meinikheim","year":"2024","unstructured":"Meinikheim, M. et al. Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett\u2019s esophagus: a tandem randomized and video trial. Endoscopy 56, 641\u2013649 (2024).","journal-title":"Endoscopy"},{"key":"2401_CR119","doi-asserted-by":"publisher","first-page":"E1118","DOI":"10.1055\/a-2404-5699","volume":"12","author":"C Robles-Medranda","year":"2024","unstructured":"Robles-Medranda, C. et al. Cholangioscopy-based convoluted neuronal network vs. confocal laser endomicroscopy in identification of neoplastic biliary strictures. Endosc. Int. Open 12, E1118\u2013E1126 (2024).","journal-title":"Endosc. Int. Open"},{"key":"2401_CR120","doi-asserted-by":"publisher","first-page":"2826","DOI":"10.1002\/lary.31255","volume":"134","author":"C Sampieri","year":"2024","unstructured":"Sampieri, C. et al. Real-time laryngeal cancer boundaries delineation on white light and narrow-band imaging laryngoscopy with deep learning. Laryngoscope 134, 2826\u20132834 (2024).","journal-title":"Laryngoscope"},{"key":"2401_CR121","doi-asserted-by":"publisher","DOI":"10.3389\/fsurg.2024.1370017","volume":"11","author":"T Sang","year":"2024","unstructured":"Sang, T. et al. A novel deep learning method to segment parathyroid glands on intraoperative videos of thyroid surgery. Front. Surg. 11, 1370017 (2024).","journal-title":"Front. Surg."},{"key":"2401_CR122","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/S2468-1253(24)00161-4","volume":"9","author":"A Seager","year":"2024","unstructured":"Seager, A. et al. Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial. Lancet Gastroenterol. Hepatol. 9, 911\u2013923 (2024).","journal-title":"Lancet Gastroenterol. Hepatol."},{"key":"2401_CR123","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/s00595-023-02756-z","volume":"54","author":"Y Shimada","year":"2024","unstructured":"Shimada, Y. et al. Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning. Surg. Today 54, 540\u2013550 (2024).","journal-title":"Surg. Today"},{"key":"2401_CR124","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.1002\/lary.31175","volume":"134","author":"X Tao","year":"2024","unstructured":"Tao, X. et al. Automatic recognition of concealed fish bones under laryngoscopy: a practical ai model based on YOLO-V5. Laryngoscope 134, 2162\u20132169 (2024).","journal-title":"Laryngoscope"},{"key":"2401_CR125","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1016\/j.dld.2024.01.177","volume":"56","author":"X Tao","year":"2024","unstructured":"Tao, X. et al. An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy. Dig. Liver Dis. 56, 1319\u20131326 (2024).","journal-title":"Dig. Liver Dis."},{"key":"2401_CR126","doi-asserted-by":"publisher","first-page":"4321","DOI":"10.1002\/lary.31537","volume":"134","author":"C-W Tie","year":"2024","unstructured":"Tie, C.-W. et al. Multi-instance learning for vocal fold leukoplakia diagnosis using white light and narrow-band imaging: a multicenter study. Laryngoscope 134, 4321\u20134328 (2024).","journal-title":"Laryngoscope"},{"key":"2401_CR127","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjoto.2024.104342","volume":"45","author":"ML Wang","year":"2024","unstructured":"Wang, M. L. et al. Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: a multicentre diagnostic study. Am. J. Otolaryngol. 45, 104342 (2024).","journal-title":"Am. J. Otolaryngol."},{"key":"2401_CR128","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1002\/hed.27629","volume":"46","author":"B Wang","year":"2024","unstructured":"Wang, B. et al. Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery. Head. Neck 46, 1975\u20131987 (2024).","journal-title":"Head. Neck"},{"key":"2401_CR129","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1055\/a-2189-7036","volume":"56","author":"J Wang","year":"2024","unstructured":"Wang, J. et al. A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation. Endoscopy 56, 260\u2013270 (2024).","journal-title":"Endoscopy"},{"key":"2401_CR130","unstructured":"Williams, S. C. et al. Artificial intelligence assisted surgical scene recognition: a comparative study amongst healthcare professionals. Ann. Surg. (2024)."},{"key":"2401_CR131","doi-asserted-by":"publisher","first-page":"878.e1","DOI":"10.1016\/j.gie.2024.06.004","volume":"100","author":"X Xie","year":"2024","unstructured":"Xie, X. et al. A new artificial intelligence system for both stomach and small-bowel capsule endoscopy. Gastrointest. Endosc. 100, 878.e1\u2013878.e14 (2024).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR132","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s10620-024-08277-0","volume":"69","author":"J Xu","year":"2024","unstructured":"Xu, J. et al. Spatio-temporal feature transformation based polyp recognition for automatic detection: higher accuracy than novice endoscopists in colorectal polyp detection and diagnosis. Dig. Dis. Sci. 69, 911\u2013921 (2024).","journal-title":"Dig. Dis. Sci."},{"key":"2401_CR133","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/bioengineering12010010","volume":"12","author":"X Xu","year":"2024","unstructured":"Xu, X. et al. Neoplasms in the nasal cavity identified and tracked with an artificial intelligence-assisted nasal endoscopic diagnostic system. Bioengineering 12, 10 (2024).","journal-title":"Bioengineering"},{"key":"2401_CR134","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/j.gie.2024.04.015","volume":"100","author":"L Yao","year":"2024","unstructured":"Yao, L. et al. Validation of artificial intelligence-based bowel preparation assessment in screening colonoscopy (with video). Gastrointest. Endosc. 100, 728\u2013736.e9 (2024).","journal-title":"Gastrointest. Endosc."},{"key":"2401_CR135","doi-asserted-by":"publisher","DOI":"10.1186\/s12894-023-01396-2","volume":"24","author":"G Zhu","year":"2024","unstructured":"Zhu, G. et al. Predicting stone composition via machine-learning models trained on intra-operative endoscopic digital images. BMC Urol. 24, 5 (2024).","journal-title":"BMC Urol."},{"key":"2401_CR136","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-83721-9","volume":"15","author":"BY Ahn","year":"2025","unstructured":"Ahn, B. Y., Lee, J., Seol, J., Kim, J. Y. & Chung, H. Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy. Sci. Rep. 15, 4693 (2025).","journal-title":"Sci. Rep."},{"key":"2401_CR137","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1111\/resp.70057","volume":"30","author":"E Amante","year":"2025","unstructured":"Amante, E. et al. Human and deep learning predictions of peripheral lung cancer using a 1.3\u2009mm video endoscopic probe. Respirology 30, 861\u2013870 (2025).","journal-title":"Respirology"},{"key":"2401_CR138","doi-asserted-by":"publisher","DOI":"10.1002\/deo2.70083","volume":"6","author":"N Aoyama","year":"2026","unstructured":"Aoyama, N. et al. Effects of artificial intelligence assistance on endoscopist performance: comparison of diagnostic performance in superficial esophageal squamous cell carcinoma detection using video-based models. DEN Open 6, e70083 (2026).","journal-title":"DEN Open"},{"key":"2401_CR139","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-025-02973-1","volume":"25","author":"J Chen","year":"2025","unstructured":"Chen, J. et al. Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video). BMC Med. Inf. Decis. Mak. 25, 144 (2025).","journal-title":"BMC Med. Inf. Decis. Mak."},{"key":"2401_CR140","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1007\/s00464-025-11527-5","volume":"39","author":"J Feng","year":"2025","unstructured":"Feng, J. et al. A prospective and comparative study on improving the diagnostic accuracy of early gastric cancer based on deep convolutional neural network real-time diagnosis system (with video). Surg. Endosc. 39, 1874\u20131884 (2025).","journal-title":"Surg. Endosc."},{"key":"2401_CR141","doi-asserted-by":"publisher","first-page":"103120","DOI":"10.1016\/j.eclinm.2025.103120","volume":"81","author":"R He","year":"2025","unstructured":"He, R. et al. Real-time artificial intelligence-assisted detection and segmentation of nasopharyngeal carcinoma using multimodal endoscopic data: a multi-center, prospective study. EClinicalMedicine 81, 103120 (2025).","journal-title":"EClinicalMedicine"},{"key":"2401_CR142","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1097\/XCS.0000000000001352","volume":"240","author":"A Khanna","year":"2025","unstructured":"Khanna, A. et al. Enhancing accuracy of operative reports with automated artificial intelligence analysis of surgical video. J. Am. Coll. Surg. 240, 739\u2013746 (2025).","journal-title":"J. Am. Coll. Surg."},{"key":"2401_CR143","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1007\/s10620-025-08879-2","volume":"70","author":"DH Kim","year":"2025","unstructured":"Kim, D. H. et al. Prospective video-based study assessing effect of computer-assisted optical diagnosis on distinguishing serrated, hyperplastic, and adenomatous colorectal polyps. Dig. Dis. Sci. 70, 1477\u20131485 (2025).","journal-title":"Dig. Dis. Sci."},{"key":"2401_CR144","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1038\/s41746-025-01532-2","volume":"8","author":"B Li","year":"2025","unstructured":"Li, B. et al. Effect of computer aided detection system on esophageal neoplasm diagnosis in varied levels of endoscopists. npj Digit. Med. 8, 160 (2025).","journal-title":"npj Digit. Med."},{"key":"2401_CR145","doi-asserted-by":"publisher","first-page":"2836","DOI":"10.1007\/s00464-025-11644-1","volume":"39","author":"R Liu","year":"2025","unstructured":"Liu, R. et al. Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video). Surg. Endosc. 39, 2836\u20132846 (2025).","journal-title":"Surg. Endosc."},{"key":"2401_CR146","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v31.i13.104370","volume":"31","author":"T Ma","year":"2025","unstructured":"Ma, T. et al. Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm. World J. Gastroenterol. 31, 104370 (2025).","journal-title":"World J. Gastroenterol."},{"key":"2401_CR147","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1038\/s41372-024-02171-3","volume":"45","author":"A Majeedi","year":"2025","unstructured":"Majeedi, A., Peebles, P. J., Li, Y. & McAdams, R. M. Glottic opening detection using deep learning for neonatal intubation with video laryngoscopy. J. Perinatol. 45, 242\u2013248 (2025).","journal-title":"J. Perinatol."},{"key":"2401_CR148","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1007\/s11548-025-03383-4","volume":"20","author":"M Mezzina","year":"2025","unstructured":"Mezzina, M. et al. Surgeons versus computer vision: a comparative analysis on surgical phase recognition capabilities. Int J. Comput Assist Radio. Surg. 20, 1283\u20131291 (2025).","journal-title":"Int J. Comput Assist Radio. Surg."},{"key":"2401_CR149","doi-asserted-by":"publisher","DOI":"10.1093\/ecco-jcc\/jjae080","volume":"19","author":"N Ogata","year":"2025","unstructured":"Ogata, N. et al. Artificial intelligence-assisted video colonoscopy for disease monitoring of ulcerative colitis: a prospective study. J. Crohns Colitis 19, jjae080 (2025).","journal-title":"J. Crohns Colitis"},{"key":"2401_CR150","doi-asserted-by":"publisher","DOI":"10.1002\/deo2.70061","volume":"5","author":"N Papachrysos","year":"2025","unstructured":"Papachrysos, N. et al. A comparative study benchmarking colon polyp with computer-aided detection (CADe) software. DEN Open 5, e70061 (2025).","journal-title":"DEN Open"},{"key":"2401_CR151","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1055\/a-2534-1164","volume":"57","author":"MW Scheppach","year":"2025","unstructured":"Scheppach, M. W. et al. Use of artificial intelligence in submucosal vessel detection during third-space endoscopy. Endoscopy 57, 760\u2013766 (2025).","journal-title":"Endoscopy"},{"key":"2401_CR152","doi-asserted-by":"crossref","unstructured":"Tappermann, C. et al. Deep-learning-based outcome prediction from endoscopic videos for robot-assisted radical prostatectomy. In Proc. Medical Imaging 2025: Image Processing 134\u2013144 (SPIE, 2025).","DOI":"10.1117\/12.3046974"},{"key":"2401_CR153","doi-asserted-by":"publisher","first-page":"e070904","DOI":"10.1136\/bmj-2022-070904","volume":"377","author":"B Vasey","year":"2022","unstructured":"Vasey, B. et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 377, e070904 (2022).","journal-title":"BMJ"},{"key":"2401_CR154","doi-asserted-by":"publisher","first-page":"i2372","DOI":"10.1136\/bmj.i2372","volume":"353","author":"A Sedrakyan","year":"2016","unstructured":"Sedrakyan, A. et al. IDEAL-D: a rational framework for evaluating and regulating the use of medical devices. BMJ 353, i2372 (2016).","journal-title":"BMJ"},{"key":"2401_CR155","doi-asserted-by":"publisher","first-page":"735","DOI":"10.2478\/amcs-2018-0056","volume":"28","author":"M Koziarski","year":"2019","unstructured":"Koziarski, M. & Cyganek, B. Impact of Low Resolution on Image Recognition with Deep Neural Networks: An Experimental Study. Int. J. Appl. Math. Comput. Sci. 28, 735\u2013744 (2019).","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"2401_CR156","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-87824-9","volume":"15","author":"D Vora","year":"2025","unstructured":"Vora, D. et al. AI-driven video summarization for optimizing content retrieval and management through deep learning techniques. Sci. Rep. 15, 4058 (2025).","journal-title":"Sci. Rep."},{"key":"2401_CR157","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).","journal-title":"BMJ"},{"key":"2401_CR158","doi-asserted-by":"publisher","first-page":"e082505","DOI":"10.1136\/bmj-2024-082505","volume":"388","author":"KGM Moons","year":"2025","unstructured":"Moons, K. G. M. et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ 388, e082505 (2025).","journal-title":"BMJ"},{"key":"2401_CR159","doi-asserted-by":"publisher","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","volume":"6","author":"T Davenport","year":"2019","unstructured":"Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Future Health. J. 6, 94\u201398 (2019).","journal-title":"Future Health. J."},{"key":"2401_CR160","unstructured":"CIEHF. Human Factors in Healthcare AI. https:\/\/ergonomics.org.uk\/resource\/human-factors-in-healthcare-ai.html (2021)."},{"key":"2401_CR161","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y. & Ghassemi, M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176\u20132182 (2021).","journal-title":"Nat. Med."},{"key":"2401_CR162","doi-asserted-by":"publisher","unstructured":"Beede, E. et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. In Proc. 2020 CHI Conference on Human Factors in Computing Systems 1\u201312 https:\/\/doi.org\/10.1145\/3313831.3376718 (ACM, 2020).","DOI":"10.1145\/3313831.3376718"},{"key":"2401_CR163","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1038\/s41746-023-00858-z","volume":"6","author":"M Mittermaier","year":"2023","unstructured":"Mittermaier, M., Raza, M. M. & Kvedar, J. C. Bias in AI-based models for medical applications: challenges and mitigation strategies. npj Digit. Med. 6, 113 (2023).","journal-title":"npj Digit. Med."},{"key":"2401_CR164","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrai.2025.100013","volume":"2","author":"FAA Caldas","year":"2025","unstructured":"Caldas, F. A. A. et al. Evaluating the performance of artificial intelligence and radiologists accuracy in breast cancer detection in screening mammography across breast densities. Eur. J. Radiol. Artif. Intell. 2, 100013 (2025).","journal-title":"Eur. J. Radiol. Artif. Intell."},{"key":"2401_CR165","doi-asserted-by":"publisher","first-page":"e599","DOI":"10.1016\/S2589-7500(21)00132-1","volume":"3","author":"AT Young","year":"2021","unstructured":"Young, A. T., Amara, D., Bhattacharya, A. & Wei, M. L. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit. Health 3, e599\u2013e611 (2021).","journal-title":"Lancet Digit. Health"},{"key":"2401_CR166","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi, M., Oakden-Rayner, L. & Beam, A. L. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit. Health 3, e745\u2013e750 (2021).","journal-title":"Lancet Digit. Health"},{"key":"2401_CR167","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3449287","volume":"5","author":"Z Bu\u00e7inca","year":"2021","unstructured":"Bu\u00e7inca, Z., Malaya, M. B. & Gajos, K. Z. To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proc. ACM Hum. Comput. Interact. 5, 1\u201321 (2021).","journal-title":"Proc. ACM Hum. Comput. Interact."},{"key":"2401_CR168","doi-asserted-by":"publisher","first-page":"129:1","DOI":"10.1145\/3579605","volume":"7","author":"H Vasconcelos","year":"2023","unstructured":"Vasconcelos, H. et al. Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact. 7, 129:1\u2013129:38 (2023).","journal-title":"Proc. ACM Hum. Comput. Interact."},{"key":"2401_CR169","doi-asserted-by":"publisher","first-page":"5010","DOI":"10.3390\/cancers13195010","volume":"13","author":"S Williams","year":"2021","unstructured":"Williams, S. et al. Artificial intelligence in brain tumour surgery-an emerging paradigm. Cancers 13, 5010 (2021).","journal-title":"Cancers"},{"key":"2401_CR170","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-09954-8","volume":"12","author":"SA Hicks","year":"2022","unstructured":"Hicks, S. A. et al. On evaluation metrics for medical applications of artificial intelligence. Sci. Rep. 12, 5979 (2022).","journal-title":"Sci. Rep."},{"key":"2401_CR171","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1038\/s41562-024-02024-1","volume":"8","author":"M Vaccaro","year":"2024","unstructured":"Vaccaro, M., Almaatouq, A. & Malone, T. When combinations of humans and AI are useful: a systematic review and meta-analysis. Nat. Hum. Behav. 8, 2293\u20132303 (2024).","journal-title":"Nat. Hum. Behav."},{"key":"2401_CR172","doi-asserted-by":"publisher","unstructured":"Campero, A. et al. A test for evaluating performance in human-computer systems. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2206.12390 (2022).","DOI":"10.48550\/arXiv.2206.12390"},{"key":"2401_CR173","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1038\/s41746-025-01559-5","volume":"8","author":"MK Wekenborg","year":"2025","unstructured":"Wekenborg, M. K., Gilbert, S. & Kather, J. N. Examining human-AI interaction in real-world healthcare beyond the laboratory. npj Digit. Med. 8, 169 (2025).","journal-title":"npj Digit. Med."},{"key":"2401_CR174","doi-asserted-by":"publisher","first-page":"e1000100","DOI":"10.1371\/journal.pmed.1000100","volume":"6","author":"A Liberati","year":"2009","unstructured":"Liberati, A. et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. PLoS Med. 6, e1000100 (2009).","journal-title":"PLoS Med."},{"key":"2401_CR175","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-028576","volume":"9","author":"S Cousins","year":"2019","unstructured":"Cousins, S., Blencowe, N. S. & Blazeby, J. M. What is an invasive procedure? A definition to inform study design, evidence synthesis and research tracking. BMJ Open 9, e028576 (2019).","journal-title":"BMJ Open"},{"key":"2401_CR176","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/biostatistics\/kxl004","volume":"8","author":"RM Harbord","year":"2007","unstructured":"Harbord, R. M., Deeks, J. J., Egger, M., Whiting, P. & Sterne, J. A. C. A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics 8, 239\u2013251 (2007).","journal-title":"Biostatistics"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02401-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02401-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02401-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:03:19Z","timestamp":1776384199000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02401-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":176,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2401"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02401-2","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,6]]},"assertion":[{"value":"10 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"D.S. is a board member of Panda Surgical, Odin Vision and EnAcuity, and is an employee of Medtronic. D.S. holds shares in Panda Surgical, Odin Vision, and EnAcuity. D.S. declares grants, which are separate to the funding for the present study, from UKRI, RAEng, Innovate UK, and EPSRC. D.S. participates on the IHU Scientific Advisory Board and the AT-NCIGT Advisory Board. H.J.M. is the Chief Medical Officer and a board member of Panda Surgical. H.J.M. holds stocks in Panda Surgical. H.J.M. declares grants, which are separate to the funding for the present study, from NIHR and EPSRC. H.J.M. holds a patent in video editing (International Publication Number WO2023017230A1). H.J.M. declares interests in Access to TouchSurgery Ecosystem, Medtronic. All other authors declare no financial or non-financial competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"323"}}