Trends in the Use of Artificial Intelligence in Surgical Simulation

Trends in the Use of Artificial Intelligence in Surgical Simulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends in the Use of Artificial Intelligence in Surgical Simulation María Inés Gaete, Julian Varas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8937038/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Technical skill is a significant determinant of postoperative outcomes, yet traditional approaches to teaching and assessing surgical performance are resource-intensive, subjective, and challenging to scale. Simulation-based training was introduced to address these limitations; however, most programs still rely on human observation and expert availability. Artificial intelligence (AI) has recently emerged as a powerful complement to simulation, offering automated performance analysis, personalized feedback, and large-scale educational analytics. This narrative review describes current trends in the use of AI in surgical simulation. We first summarize innovations in machine learning and deep learning, natural language processing, and skills and performance analysis. We then outline key educational opportunities related to technical skills training, assessment, debriefing, and quality improvement. Implementation strategies and challenges are discussed, including institutional buy-in, funding and infrastructure, integration into existing curricula, and faculty development. Finally, we highlight future directions such as multimodal foundation models, generative simulation, global expansion into low and middle-income settings, and research priorities for rigorous validation and equity. Across multiple studies, AI-based systems can accurately classify expertise, segment procedures, and predict scores on validated rating scales. Randomized trials also show that AI tutoring can match or exceed expert instruction in simulated tasks. However, unintended effects, algorithmic bias, and limited evidence of long-term educational impact underscore the need for thoughtful implementation and sustained human oversight. AI, at least over the next 5 years, should be understood as an augmenting technology that can enhance, but not replace, expert surgical educators. artificial intelligence surgical simulation machine learning technical skills assessment feedback Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 23 Feb, 2026 Editor invited by journal 22 Feb, 2026 First submitted to journal 12 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8937038","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595868660,"identity":"83fa3ce3-0319-4be5-9dd7-0a8796e99c28","order_by":0,"name":"María Inés Gaete","email":"","orcid":"","institution":"Pontificia Universidad Católica de Chile: Pontificia Universidad Catolica de Chile","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Inés","lastName":"Gaete","suffix":""},{"id":595868661,"identity":"66f16381-7d1c-4d7f-95fe-1baa549ac57e","order_by":1,"name":"Julian Varas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoklEQVRIiWNgGAWjYBACxgYILQcmH5CixRhMJpBiW2ID0VqY23sMP1fU1KVvOH6A8QNRWhh7zhhLnjl2OHfDmQRmCeK0zEhLkGxgO5C74QYDG3EOY5z/LPlnw7+6dAPitcxgPibZ2MacQIKWnuRjlo19hw1nnklsJs4vhu0Hm282fKuT5zt++OCHD0RpaUBY2IBTFQqQJ07ZKBgFo2AUjGgAAAlwM3JRin9QAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5828-9623","institution":"Pontificia Universidad Catolica de Chile Escuela de Medicina","correspondingAuthor":true,"prefix":"","firstName":"Julian","middleName":"","lastName":"Varas","suffix":""}],"badges":[],"createdAt":"2026-02-22 05:25:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8937038/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8937038/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103531754,"identity":"e6e00f02-1cce-4273-8749-efb5d91168d4","added_by":"auto","created_at":"2026-02-26 17:16:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":278875,"visible":true,"origin":"","legend":"","description":"","filename":"BlindedmanuscriptTrendsAIinSS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8937038/v1_covered_90edc66a-7419-4e44-b03e-6d7adf396389.pdf"}],"financialInterests":"","formattedTitle":"Trends in the Use of Artificial Intelligence in Surgical Simulation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"global-surgical-education-journal-of-the-association-for-surgical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"GSED","sideBox":"Learn more about [Global Surgical Education - Journal of the Association for Surgical Education](https://link.springer.com/journal/44186)","snPcode":"44186","submissionUrl":"https://www.editorialmanager.com/gsed/default1.aspx","title":"Global Surgical Education - Journal of the Association for Surgical Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"artificial intelligence, surgical simulation, machine learning, technical skills, assessment, feedback","lastPublishedDoi":"10.21203/rs.3.rs-8937038/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8937038/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Technical skill is a significant determinant of postoperative outcomes, yet traditional approaches to teaching and assessing surgical performance are resource-intensive, subjective, and challenging to scale. 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