{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:59:55Z","timestamp":1785373195237,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100019266","name":"Korea Medical Device Development Fund","doi-asserted-by":"publisher","award":["2026-RISE-01-014-05"],"award-info":[{"award-number":["2026-RISE-01-014-05"]}],"id":[{"id":"10.13039\/100019266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015825","name":"Korea University Anam Hospital","doi-asserted-by":"publisher","award":["O2616771"],"award-info":[{"award-number":["O2616771"]}],"id":[{"id":"10.13039\/501100015825","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015825","name":"Korea University Anam Hospital","doi-asserted-by":"publisher","award":["O2617451"],"award-info":[{"award-number":["O2617451"]}],"id":[{"id":"10.13039\/501100015825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["UEG Journal"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate histologic classification of ampullary lesions is essential for guiding therapeutic decisions; however, conventional biopsy is limited by sampling error and false\u2010negative results. We evaluated the clinical utility of a generative artificial intelligence (AI)\u2013based computer\u2010aided diagnosis (CAD) system that integrates real and synthetic endoscopic images to improve diagnostic performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective multicenter study conducted across seven hospitals, duodenoscopic images were classified as Normal, Adenoma, or Cancer. A generative AI\u2010based CAD system using latent diffusion synthesized 500 images per class for data augmentation and was trained to predict histologic classes from endoscopic images. External validation assessed accuracy, sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curve. A reader study involving five expert and five trainee endoscopists compared diagnostic performance with and without CAD assistance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The generative AI\u2013based CAD system demonstrated high overall diagnostic accuracy (91.57%) and strong performance in identifying adenomas (accuracy, 88.76%). In the reader study, CAD assistance significantly increased adenoma sensitivity (63.47%\u201370.56%;\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.01), with corresponding improvements in predictive values. Both expert and trainee endoscopists benefited from CAD support, with reduced interobserver variability and consistent improvements across Normal, Adenoma, and Cancer classifications.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A generative AI\u2013based CAD system improved diagnostic accuracy and consistency in the evaluation of ampullary lesions. These findings support its potential as a clinically useful adjunct to routine duodenoscopy, particularly for improving adenoma recognition and supporting therapeutic decision\u2010making.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ueg2.70249","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T06:32:54Z","timestamp":1782196374000},"source":"Crossref","is-referenced-by-count":0,"title":["Clinical Validation of a Generative AI System for Diagnosing Ampullary Lesions: A Multicenter Study"],"prefix":"10.1002","volume":"14","author":[{"given":"Jang Ho","family":"Kwon","sequence":"first","affiliation":[{"name":"Seoul National University of Science and Technology  Seoul Republic of Korea"},{"name":"Hanyang University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho Seung","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Korea University Anam Hospital  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Hoon","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Medicine Samsung Medical Center  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Min","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Korea University Anam Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyoung Joo","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Department of Internal Medicine Hallym University Dongtan Sacred Heart Hospital  Hwaseong\u2010si Gyeonggi\u2010do Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jai Hoon","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Hanyang University Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8710-8400","authenticated-orcid":false,"given":"Dong\u2010Sup","family":"Jin","sequence":"additional","affiliation":[{"name":"University of Ulsan  Ulsan Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyo Jung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Korea University Guro Hospital  Seoul Republic of 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