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Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high\u2010quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.<\/jats:p>","DOI":"10.1002\/deo2.267","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T13:31:34Z","timestamp":1688131894000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8348-8926","authenticated-orcid":false,"given":"Takamichi","family":"Kuwahara","sequence":"first","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuo","family":"Hara","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobumasa","family":"Mizuno","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin","family":"Haba","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nozomi","family":"Okuno","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshitaka","family":"Fukui","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minako","family":"Urata","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshitaro","family":"Yamamoto","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology Aichi Cancer Center Hospital Aichi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"e_1_2_10_2_1","volume-title":"Digestive System Tumors. 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