{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T19:18:50Z","timestamp":1788808730539,"version":"build-2803163510"},"reference-count":15,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Annals of Gastroent Surgery"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>We evaluated the educational impact of artificial intelligence (AI)\u2010navigation surgery for medical students which provides real\u2010time anatomical landmark recognition during laparoscopic cholecystectomy (LC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Thirty fifth\u2010year medical students were randomly assigned to three groups: surgeon\u2010guided (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910), self\u2010learning (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910), and AI\u2010learning (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200910). Each group annotated anatomical landmarks, extrahepatic bile duct (EHBD), cystic duct (CD), Rouvi\u00e8re's sulcus (RS), the base of liver segment 4 (S4), before and after training. The AI\u2010learning group received real\u2010time feedback using a deep learning segmentation model (HyperSeg). Learning outcomes were quantitatively assessed and compared to expert annotations using Dice coefficients, and post\u2010study questionnaires were analyzed to evaluate understanding of anatomy and surgical procedures.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The mean Dice coefficients in the surgeon\u2010guided (0.450\u2009\u00b1\u20090.025) and AI\u2010learning groups (0.432\u2009\u00b1\u20090.038) were significantly higher in comparison to the self\u2010learning group (0.351\u2009\u00b1\u20090.057,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.00006). In an itemized analysis, significant improvements were observed in EHBD and RS recognition, but not in CD or S4 recognition. In the post\u2010study questionnaire assessing anatomical understanding and the ability to comprehend the surgeon's perspective and intentions, the surgeon\u2010guided group showed significantly better results in comparison to the self\u2010learning group (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001 for each comparison). However, there was no significant difference between the AI\u2010learning and self\u2010learning groups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI has the potential to complement surgeon's guidance, reducing faculty burden while maintaining educational quality in surgical education.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ags3.70149","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T06:25:26Z","timestamp":1765866326000},"page":"843-850","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Educational Impact of Artificial Intelligence\u2010Navigation Surgery on Anatomical Landmark Recognition in Medical Students"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3608-2146","authenticated-orcid":false,"given":"Shigeo","family":"Ninomiya","sequence":"first","affiliation":[{"name":"Department of Gastroenterological and Pediatric Surgery Oita University Faculty of Medicine  Yufu Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3404-8080","authenticated-orcid":false,"given":"Yusuke","family":"Matsunobu","sequence":"additional","affiliation":[{"name":"Department of Healthcare AI Data Science Oita University Faculty of Medicine  Yufu Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7816-550X","authenticated-orcid":false,"given":"Tabito","family":"Oyama","sequence":"additional","affiliation":[{"name":"Department of Gastroenterological and Pediatric Surgery Oita University Faculty of Medicine  Yufu Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroomi","family":"Takayama","sequence":"additional","affiliation":[{"name":"Department of Comprehensive Surgery for Community Medicine Oita University Faculty of Medicine  Yufu Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Masuda","sequence":"additional","affiliation":[{"name":"Department of Gastroenterological and Pediatric Surgery Oita University Faculty of Medicine  Yufu Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0252-0494","authenticated-orcid":false,"given":"Teijiro","family":"Hirashita","sequence":"additional","affiliation":[{"name":"Department of Gastroenterological and Pediatric Surgery Oita University Faculty of Medicine  Yufu 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