{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T10:27:33Z","timestamp":1794479253476,"version":"4.9.1"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,10,5]],"date-time":"2027-10-05T00:00:00Z","timestamp":1822694400000},"content-version":"am","delay-in-days":308,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["5R01DK123445-04"],"award-info":[{"award-number":["5R01DK123445-04"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["americanjournalofsurgery.com","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The American Journal of Surgery"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.amjsurg.2026.117261","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T15:34:03Z","timestamp":1790350443000},"page":"117261","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Evolution of artificial intelligence integration into surgical workflows"],"prefix":"10.1016","volume":"262","author":[{"given":"Souma","family":"Kundu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua A.","family":"Villarreal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Calvin","family":"Perumalla","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9139-8082","authenticated-orcid":false,"given":"Carla","family":"Pugh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.amjsurg.2026.117261_bib1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.amjsurg.2020.06.027","article-title":"The what? How? And who? Of video based assessment","volume":"221","author":"Pugh","year":"2021","journal-title":"Am J Surg"},{"issue":"6","key":"10.1016\/j.amjsurg.2026.117261_bib2","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1097\/SLA.0000000000005695","article-title":"The operating room black box: understanding adherence to surgical checklists","volume":"276","author":"Al Abbas","year":"2022","journal-title":"Ann Surg"},{"issue":"1","key":"10.1016\/j.amjsurg.2026.117261_bib3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13037-024-00406-y","article-title":"Artificial intelligence model for automated surgical instrument detection and counting: an experimental proof-of-concept study","volume":"18","author":"Deol","year":"2024","journal-title":"Patient Saf Surg"},{"issue":"5","key":"10.1016\/j.amjsurg.2026.117261_bib4","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1097\/XCS.0000000000001352","article-title":"Enhancing accuracy of operative reports with automated artificial intelligence analysis of surgical video","volume":"240","author":"Khanna","year":"2025","journal-title":"J Am Coll Surg"},{"issue":"3","key":"10.1016\/j.amjsurg.2026.117261_bib5","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1097\/SLA.0000000000003460","article-title":"Computer vision analysis of intraoperative video: automated recognition of operative steps in laparoscopic sleeve gastrectomy","volume":"270","author":"Hashimoto","year":"2019","journal-title":"Ann Surg"},{"issue":"3","key":"10.1016\/j.amjsurg.2026.117261_bib6","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1097\/SLA.0000000000006377","article-title":"Surgical intelligence can lead to higher adoption of best practices in minimally invasive surgery","volume":"280","author":"Fried","year":"2024","journal-title":"Ann Surg"},{"issue":"4","key":"10.1016\/j.amjsurg.2026.117261_bib7","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1007\/s11548-025-03564-1","article-title":"Enhancing open-surgery gesture recognition using 3D pose estimation","volume":"21","author":"Meiraz","year":"2026","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"2","key":"10.1016\/j.amjsurg.2026.117261_bib8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1001\/jamasurg.2023.6262","article-title":"Analyzing surgical technique in diverse open surgical videos with multitask machine learning","volume":"159","author":"Goodman","year":"2024","journal-title":"JAMA Surg"},{"issue":"1","key":"10.1016\/j.amjsurg.2026.117261_bib9","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41746-022-00566-0","article-title":"Machine learning for technical skill assessment in surgery: a systematic review","volume":"5","author":"Lam","year":"2022","journal-title":"npj Digit Med"},{"issue":"1","key":"10.1016\/j.amjsurg.2026.117261_bib10","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.juro.2017.07.081","article-title":"Development and validation of objective performance metrics for robot-assisted radical prostatectomy: a pilot study","volume":"199","author":"Hung","year":"2018","journal-title":"J Urol"},{"issue":"4","key":"10.1016\/j.amjsurg.2026.117261_bib11","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.juro.2018.05.080","article-title":"Use of automated performance metrics to measure surgeon performance during robotic vesicourethral anastomosis and methodical development of a training tutorial","volume":"200","author":"Chen","year":"2018","journal-title":"J Urol"},{"key":"10.1016\/j.amjsurg.2026.117261_bib12","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.jss.2022.10.069","article-title":"AI-Based video segmentation: procedural steps or basic maneuvers?","volume":"283","author":"Perumalla","year":"2023","journal-title":"J Surg Res"},{"issue":"7","key":"10.1016\/j.amjsurg.2026.117261_bib13","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.media.2013.04.007","article-title":"Surgical gesture classification from video and kinematic data","volume":"17","author":"Zappella","year":"2013","journal-title":"Med Image Anal"},{"issue":"6","key":"10.1016\/j.amjsurg.2026.117261_bib14","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1038\/s41551-023-01010-8","article-title":"A vision transformer for decoding surgeon activity from surgical videos","volume":"7","author":"Kiyasseh","year":"2023","journal-title":"Nat Biomed Eng"},{"issue":"4","key":"10.1016\/j.amjsurg.2026.117261_bib15","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1097\/XCS.0000000000001286","article-title":"Novel use of objective sensor technology: creation of individualized education plans to develop operative mastery","volume":"240","author":"Korndorffer","year":"2025","journal-title":"J Am Coll Surg"},{"issue":"1","key":"10.1016\/j.amjsurg.2026.117261_bib16","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s43856-023-00263-3","article-title":"A multi-institutional study using artificial intelligence to provide reliable and fair feedback to surgeons","volume":"3","author":"Kiyasseh","year":"2023","journal-title":"Commun Med"},{"issue":"10","key":"10.1016\/j.amjsurg.2026.117261_bib17","doi-asserted-by":"crossref","first-page":"7336","DOI":"10.1097\/JS9.0000000000002772","article-title":"Real-time AI-based detection of excessive traction on the recurrent laryngeal nerve during robot-assisted minimally invasive esophagectomy: a proof-of-concept study","volume":"111","author":"Furube","year":"2025","journal-title":"Int J Surg"},{"issue":"6","key":"10.1016\/j.amjsurg.2026.117261_bib18","doi-asserted-by":"crossref","first-page":"3461","DOI":"10.1007\/s00464-024-10874-z","article-title":"Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence","volume":"38","author":"Horita","year":"2024","journal-title":"Surg Endosc"},{"issue":"5","key":"10.1016\/j.amjsurg.2026.117261_bib19","doi-asserted-by":"crossref","first-page":"1396","DOI":"10.1016\/j.surg.2024.07.015","article-title":"The use of artificial intelligence to detect parathyroid tissue on ex vivo specimens during thyroidectomy and parathyroidectomy procedures using near-infrared autofluorescence signals","volume":"176","author":"Akgun","year":"2024","journal-title":"Surgery"},{"key":"10.1016\/j.amjsurg.2026.117261_bib20","article-title":"Standardization of surgical gesture taxonomy: a SAGES Delphi consensus study","volume":"15","author":"Morais","year":"2026","journal-title":"Surg Endosc"},{"issue":"9","key":"10.1016\/j.amjsurg.2026.117261_bib21","first-page":"1003","article-title":"Intelligent, human-centric delivery is needed to maximize AI","volume":"160","author":"Pugh","year":"2025","journal-title":"JAMA"},{"issue":"1","key":"10.1016\/j.amjsurg.2026.117261_bib22","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s00464-017-5662-2","article-title":"A structured, extended training program to facilitate adoption of new techniques for practicing surgeons","volume":"32","author":"Greenberg","year":"2018","journal-title":"Surg Endosc"},{"issue":"11","key":"10.1016\/j.amjsurg.2026.117261_bib23","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1016\/j.jsurg.2024.08.019","article-title":"The film room: using artificial intelligence to facilitate video review for urology trainees","volume":"81","author":"Henning","year":"2024","journal-title":"J Surg Educ"},{"issue":"5","key":"10.1016\/j.amjsurg.2026.117261_bib24","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1089\/end.2018.0035","article-title":"Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes","volume":"32","author":"Hung","year":"2018","journal-title":"J Endourol"},{"issue":"1","key":"10.1016\/j.amjsurg.2026.117261_bib25","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1038\/s41746-022-00738-y","article-title":"Surgical gestures as a method to quantify surgical performance and predict patient outcomes","volume":"5","author":"Ma","year":"2022","journal-title":"npj Digit Med"},{"issue":"4","key":"10.1016\/j.amjsurg.2026.117261_bib26","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1097\/SLA.0000000000005595","article-title":"Do individual surgeon preferences affect procedural outcomes?","volume":"276","author":"Mohamadipanah","year":"2022","journal-title":"Ann Surg"}],"container-title":["The American Journal of Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0002961026004460?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0002961026004460?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T09:39:29Z","timestamp":1794476369000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0002961026004460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":26,"alternative-id":["S0002961026004460"],"URL":"https:\/\/doi.org\/10.1016\/j.amjsurg.2026.117261","relation":{},"ISSN":["0002-9610"],"issn-type":[{"value":"0002-9610","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evolution of artificial intelligence integration into surgical workflows","name":"articletitle","label":"Article Title"},{"value":"The American Journal of Surgery","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.amjsurg.2026.117261","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"117261"}}