{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T07:48:36Z","timestamp":1791618516065,"version":"4.3.4"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Adenoma detection rate (ADR) is a key indicator of colonoscopy quality, but operator variability persists. Female clinicians may perform differently in other medical contexts, and artificial intelligence (AI) has been proposed to enhance detection. This study evaluated the impact of endoscopist characteristics and AI assistance on ADR in a real-world cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study analyzed colonoscopy data from 17,604 patients in a single center in China between January 2021 and August 2022. Endoscopist characteristics were determined via survey, and AI-assisted procedures were identified based on implementation dates. The primary outcome was ADR; secondary outcomes included polyp detection rate (PDR), lesion size and location, and detection rates of advanced adenomas (AA) and sessile serrated lesions (SSL). Multivariable regression models were employed to adjust for confounders, including patient and endoscopist characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of the 17,604 colonoscopies, 12,638 (71.8%) were performed by male endoscopists and 4,966 (28.2%) by female endoscopists. After adjustment by nine confounders at patient and endoscopist levels and the use of AI, female endoscopists achieved higher ADR (AOR 1.273, 95% CI 1.171 to 1.385;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001) and PDR (AOR 1.816, 95% CI 1.679 to 1.964;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). Female endoscopists also outperformed in detecting diminutive adenomas and lesions in the proximal colon. AI assistance improved ADR among male endoscopists (AOR 1.166, 95% CI 1.064 to 1.278;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.001) but had no significant effect for females. With AI, ADR among male endoscopists increased to 28.77%, approaching the level observed in female endoscopists without AI (30.05%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Endoscopist characteristics, particularly sex and experience, and AI assistance influence ADR. Female endoscopists demonstrated superior performance in detecting lesions during colonoscopy, especially for small and proximally located lesions. AI assistance significantly enhanced the performance of male endoscopists, reducing sex-based disparities in ADR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1720617","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T08:15:25Z","timestamp":1768205725000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring the influence of endoscopist characteristics and artificial intelligence on adenoma detection in colonoscopy"],"prefix":"10.3389","volume":"12","author":[{"given":"Chenxia","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dexin","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zehua","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiejun","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huang","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianlian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1053\/j.gastro.2019.06.043","article-title":"Strategies 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