{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T00:47:31Z","timestamp":1794358051477,"version":"4.9.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100005405","name":"American Surgical Association Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005405","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Surg Endosc"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is increasingly utilized in surgical care for decision support, operative planning, intraoperative guidance, and autonomous functions. While these systems can enhance efficiency and clinical performance, they also introduce risks related to technology, human factors, legal issues, and ethics. Current regulatory and legal frameworks are not fully equipped to address the challenges of AI-assisted surgery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This SAGES white paper synthesizes current regulatory, legal, ethical, and clinical considerations relevant to the deployment of AI systems in surgery. We review market safety regulation, data privacy law, informed consent, malpractice and liability principles, and propose a conceptual framework for understanding risk in surgical AI implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Risks associated with surgical AI can be understood through a tripartite framework: risks inherent to the AI system, risks introduced by the clinician-user, and risks arising from institutional deployment. These risks may manifest clinically as diagnostic error, treatment error, compromised informed consent, erosion of patient trust, threats to therapeutic autonomy, and privacy violations. Although surgeons remain the ultimate clinical decision-makers, liability may also extend to developers for defective design or failure to warn, and to institutions for negligent implementation or oversight.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The safe integration of AI into surgery requires more than technical performance alone. Robust governance, ongoing performance surveillance, incident response pathways, clinician credentialing, and specialty-society engagement are needed to reduce harm and clarify accountability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00464-026-12881-8","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T20:43:25Z","timestamp":1780951405000},"page":"5468-5479","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Risk and liability in the deployment of AI systems for surgery: a SAGES white paper"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4725-3104","authenticated-orcid":false,"given":"Daniel A.","family":"Hashimoto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayson S.","family":"Marwaha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharon A.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Schwaitzberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mindy N.","family":"Duffourc","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"12881_CR1","unstructured":"EU Artificial Intelligence Act (2025) Article 3: Definitions. EU artificial intelligence act. https:\/\/artificialintelligenceact.eu\/article\/3\/. Accessed 20 Sept 2025"},{"key":"12881_CR2","unstructured":"US Food and Drug Administration (2019) Proposed regulatory framework for modifications to artificial intelligence\/machine learning (AI\/ML)-based software as a medical device (SaMD) - discussion paper and request for feedback. Docket No. FDA-2019-N-1185. Regulations.gov. https:\/\/www.regulations.gov\/docket\/FDA-2019-N-1185\/document. Accessed 15 Sept 2025"},{"key":"12881_CR3","unstructured":"US Food and Drug Administration (2024) International Medical Device Regulators Forum (IMDRF). Center for Devices and Radiological Health, FDA. https:\/\/www.fda.gov\/medical-devices\/cdrh-international-affairs\/international-medical-device-regulators-forum-imdrf. Accessed 15 Sept 2025"},{"key":"12881_CR4","unstructured":"International Medical Device Regulators Forum (2014) Software as a medical device: possible framework for risk categorization and corresponding considerations. IMDRF\/SaMD WG\/N12FINAL:2014. https:\/\/www.imdrf.org\/documents\/software-medical-device-possible-framework-risk-categorization-and-corresponding-considerations. Accessed 16 Sept 2025"},{"key":"12881_CR5","unstructured":"US Food and Drug Administration (2025) Marketing submission recommendations for a predetermined change control plan for artificial intelligence-enabled device software functions: guidance for industry and Food and Drug Administration staff. Docket No. FDA-2022-D-2628. Center for Devices and Radiological Health, FDA. https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/marketing-submission-recommendations-predetermined-change-control-plan-artificial-intelligence. Accessed 20 Sept 2025"},{"key":"12881_CR6","unstructured":"Madiega T (2024) Artificial intelligence act. EU legislation in progress briefing, PE 698.792. European parliamentary research service, European Parliament. https:\/\/www.europarl.europa.eu\/thinktank\/en\/document\/EPRS_BRI(2021)698792. Accessed 20 Sept 2025"},{"key":"12881_CR7","unstructured":"Wolford B (2018) What is GDPR, the EU\u2019s new data protection law? GDPR.eu https:\/\/gdpr.eu\/what-is-gdpr\/. Accessed 20 Sept 2025"},{"key":"12881_CR8","unstructured":"US Department of Health and Human Services, Office for Civil Rights (2024) The HIPAA privacy rule. HHS.gov. https:\/\/www.hhs.gov\/hipaa\/for-professionals\/privacy\/index.html. Accessed 15 Sept 2025"},{"key":"12881_CR9","doi-asserted-by":"publisher","DOI":"10.1177\/00222429251314491","author":"SM Tully","year":"2025","unstructured":"Tully SM, Longoni C, Appel G (2025) Lower artificial intelligence literacy predicts greater AI receptivity. J Mark. https:\/\/doi.org\/10.1177\/00222429251314491","journal-title":"J Mark"},{"key":"12881_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17351\/ests2020.277","volume":"6","author":"A Campolo","year":"2020","unstructured":"Campolo A, Crawford K (2020) Enchanted determinism: power without responsibility in artificial intelligence. Engaging STS 6:1\u201319","journal-title":"Engaging STS"},{"key":"12881_CR11","unstructured":"Cohen IG (2020) Informed consent and medical artificial intelligence: what to tell the patient? Georgetown Law J 108(6):1425\u20131469. https:\/\/www.law.georgetown.edu\/georgetown-law-journal\/in-print\/volume-108\/volume-108-issue-6-june-2020\/informed-consent-and-medical-artificial-intelligence-what-to-tell-the-patient\/"},{"key":"12881_CR12","doi-asserted-by":"publisher","unstructured":"Yang GZ, Cambias J, Cleary K, Daimler E, Drake J, Dupont PE, Hata N, Kazanzides P, Martel S, Patel RV, Santos VJ, Taylor RH (2017) Medical robotics\u2014regulatory, ethical, and legal considerations for increasing levels of autonomy. Sci Robot 2:eaam8638. https:\/\/doi.org\/10.1126\/scirobotics.aam8638","DOI":"10.1126\/scirobotics.aam8638"},{"key":"12881_CR13","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1001\/amajethics.2019.138","volume":"21","author":"D Schiff","year":"2019","unstructured":"Schiff D, Borenstein J (2019) How should clinicians communicate with patients about the roles of artificially intelligent team members? AMA J Ethics 21:138\u2013145","journal-title":"AMA J Ethics"},{"key":"12881_CR14","doi-asserted-by":"crossref","unstructured":"Price WN, Gerke S, Cohen IG (2024) Liability for use of articficial intelligence in medicine. In: Solaiman B, Cohen IG (eds) Research handbook on health, AI and the law. Edward Elgar Publishing, Cheltenham, UK, pp 150\u2013166","DOI":"10.4337\/9781802205657.00016"},{"key":"12881_CR15","doi-asserted-by":"publisher","unstructured":"Slattery P, Saeri AK, Grundy EAC, Graham J, Noetel M, Uuk R, Dao J, Pour S, Casper S, Thompson N (2026) The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence. Patterns 7(5):101517. https:\/\/doi.org\/10.1016\/j.patter.2026.101517","DOI":"10.1016\/j.patter.2026.101517"},{"key":"12881_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000651","volume":"3","author":"JL Cross","year":"2024","unstructured":"Cross JL, Choma MA, Onofrey JA (2024) Bias in medical AI: implications for clinical decision-making. PLoS Digit Health 3:e0000651","journal-title":"PLoS Digit Health"},{"key":"12881_CR17","volume":"6","author":"F Neha","year":"2025","unstructured":"Neha F, Bhati D, Shukla DK (2025) Retrieval-augmented generation (RAG) in healthcare: a comprehensive review. AI (Basel) 6:226","journal-title":"AI (Basel)"},{"key":"12881_CR18","doi-asserted-by":"crossref","unstructured":"Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-N\u00f6tzel D, Kavur AE, R\u00e4dsch T, Sudre CH, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko M, Buettner F, Cardoso MJ, Cheplygina V, Chen J, Christodoulou E, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Glocker B, Godau P, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Isensee F, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Kleesiek J, Kofler F, Kooi T, Kopp-Schneider A, Kozubek M, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Meijering E, Menze B, Moons KGM, M\u00fcller H, Nichyporuk B, Nickel F, Petersen J, Rafelski SM, Rajpoot N, Reyes M, Riegler MA, Rieke N, Saez-Rodriguez J, S\u00e1nchez CI, Shetty S, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Yaniv ZR, J\u00e4ger PF, Maier-Hein L (2024) Understanding metric-related pitfalls in image analysis validation. Nat Methods 21:182\u2013194","DOI":"10.1038\/s41592-023-02150-0"},{"key":"12881_CR19","doi-asserted-by":"crossref","unstructured":"Madani A, Vassiliou MC, Watanabe Y, Al-Halabi B, Al-Rowais MS, Deckelbaum DL, Fried GM, Feldman LS (2017) What are the principles that guide behaviors in the operating room?: Creating a framework to define and measure performance. Ann Surg 265:255\u2013267","DOI":"10.1097\/SLA.0000000000001962"},{"key":"12881_CR20","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1097\/01.SLA.0000060680.92690.E9","volume":"237","author":"LW Way","year":"2003","unstructured":"Way LW et al (2003) Causes and prevention of laparoscopic bile duct injuries: analysis of 252 cases from a human factors and cognitive psychology perspective. Ann Surg 237:460\u2013469","journal-title":"Ann Surg"},{"key":"12881_CR21","doi-asserted-by":"publisher","unstructured":"Kim SSY, Liao QV, Vorvoreanu M, Ballard, S, Vaughan JW (2024) \u2018I\u2019m not sure, but...\u2019: examining the impact of large language models\u2019 uncertainty expression on user reliance and trust. FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp 822\u2013835. https:\/\/doi.org\/10.1145\/3630106.3658941","DOI":"10.1145\/3630106.3658941"},{"key":"12881_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1337335","volume":"10","author":"C Terranova","year":"2024","unstructured":"Terranova C, Cestonaro C, Fava L, Cinquetti A (2024) AI and professional liability assessment in healthcare. A revolution in legal medicine? Front Med 10:1337335","journal-title":"Front Med"},{"key":"12881_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-025-01544-y","volume":"8","author":"GE Weissman","year":"2025","unstructured":"Weissman GE, Mankowitz T, Kanter GP (2025) Unregulated large language models produce medical device-like output. npj Digit Med 8:1\u20135","journal-title":"npj Digit Med"},{"key":"12881_CR24","unstructured":"Schneier B, Sanders NE (2025) AI mistakes are way weirder than human mistakes. IEEE Spectrum. https:\/\/spectrum.ieee.org\/ai-mistakes-schneier. Accessed 22 Sept 2025"},{"key":"12881_CR25","unstructured":"Duffourc MN (2023) Malpractice by the autonomous AI physician. Ill J Law Technol Policy 2023(1):1\u201362"},{"key":"12881_CR26","doi-asserted-by":"crossref","unstructured":"Jackson BR, Ye Y, Crawford JM, Becich MJ, Roy S, Botkin JR, de Baca ME, Pantanowitz L (2021) The ethics of artificial intelligence in pathology and laboratory medicine: principles and practice. Acad Pathol 8:2374289521990784","DOI":"10.1177\/2374289521990784"},{"key":"12881_CR27","unstructured":"Vollmer N (2023) Recital 26 EU general data protection regulation (EU-GDPR). Privacy-Regulation.eu. https:\/\/www.privacy-regulation.eu\/en\/recital-26-GDPR.htm. Accessed 25 Sept 2025"},{"key":"12881_CR28","doi-asserted-by":"crossref","unstructured":"Hoffman S (2019) Artificial intelligence in medicine raises legal and ethical concerns. The conversation. http:\/\/theconversation.com\/artificial-intelligence-in-medicine-raises-legal-and-ethical-concerns-122504. Accessed 12 Sept 2025","DOI":"10.64628\/AAI.vcsrtrwdp"},{"key":"12881_CR29","doi-asserted-by":"crossref","unstructured":"Duffourc MN, Giovanniello DS (2024) The autonomous AI physician: medical ethics and legal liability. In: Sousa Antunes H, Freitas PM, Oliveira AL, Martins Pereira C, Vaz de Sequeira E, Barreto Xavier L (eds) Multidisciplinary perspectives on artificial intelligence and the law. Law, Governance and Technology Series, vol 58. Springer, Cham, pp 207\u2013228","DOI":"10.1007\/978-3-031-41264-6_11"},{"key":"12881_CR30","doi-asserted-by":"crossref","unstructured":"Gerke S, Minssen T, Cohen IG (2020) Ethical and legal challenges of artificial intelligence-driven healthcare. In: Bohr A, Memarzadeh K (eds) Artificial intelligence in healthcare. Academic Press, Cambridge, MA, pp 295\u2013336","DOI":"10.1016\/B978-0-12-818438-7.00012-5"},{"key":"#cr-split#-12881_CR31.1","unstructured":"European Parliament and Council of the European Union (2016) Regulation"},{"key":"#cr-split#-12881_CR31.2","unstructured":"(EU) 2016\/679 of the European parliament and of the council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95\/46\/EC (general data protection regulation). OJ L 119:1-88. https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj\/eng. Accessed 25 Sept 2025"},{"key":"12881_CR32","doi-asserted-by":"publisher","DOI":"10.1097\/AS9.0000000000000542","volume":"6","author":"M Duffourc","year":"2025","unstructured":"Duffourc M, M\u00f8lleb\u00e6k M, Druedahl LC, Minssen T, Gerke S (2025) Surgeons\u2019 perspectives on liability for the use of artificial intelligence technologies in the United States and European Union: results from a focus group study. Ann Surg Open 6:e542","journal-title":"Ann Surg Open"},{"key":"12881_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-023-00929-1","volume":"6","author":"AD Saenz","year":"2023","unstructured":"Saenz AD, Harned Z, Banerjee O, Abr\u00e0moff MD, Rajpurkar P (2023) Autonomous AI systems in the face of liability, regulations and costs. npj Digit Med 6:1\u20133","journal-title":"npj Digit Med"},{"key":"12881_CR34","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1111\/1468-0009.12504","volume":"99","author":"G Maliha","year":"2021","unstructured":"Maliha G, Gerke S, Glenn Cohen I, Parikh RB (2021) Artificial intelligence and liability in medicine: balancing safety and innovation. Milbank Q 99:629\u2013647","journal-title":"Milbank Q"},{"key":"12881_CR35","doi-asserted-by":"publisher","first-page":"17","DOI":"10.2967\/jnumed.120.256032","volume":"62","author":"K Tobia","year":"2021","unstructured":"Tobia K, Nielsen A, Stremitzer A (2021) When does physician use of AI increase liability? J Nucl Med 62:17\u201321","journal-title":"J Nucl Med"},{"key":"12881_CR36","doi-asserted-by":"crossref","unstructured":"Shamsujjoha Md, Qinghua Lu, Dehai Z, Liming Z (2025) Swiss cheese model for AI safety: a taxonomy and reference architecture for multi-layered guardrails of foundation model based agents. In: IEEE 22nd international conference on software architecture (ICSA), IEEE, pp  37\u201348","DOI":"10.1109\/ICSA65012.2025.00014"},{"key":"12881_CR37","doi-asserted-by":"publisher","DOI":"10.1056\/AIp2400109","author":"AN Beecy","year":"2024","unstructured":"Beecy AN, Longhurst CA, Singh K, Wachter RM, Murray SG (2024) The chief health AI officer \u2014 An emerging role for an emerging technology. NEJM AI. https:\/\/doi.org\/10.1056\/AIp2400109","journal-title":"NEJM AI"},{"key":"12881_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-025-01960-0","volume":"8","author":"JS Marwaha","year":"2025","unstructured":"Marwaha JS, Yuan W, Poddar M, Elsamadisi P, Brat GA (2025) The algorithmic consultant: A new era of clinical AI calls for a new workforce of physician-algorithm specialists. npj Digit Med 8:1\u20135","journal-title":"npj Digit Med"},{"key":"12881_CR39","unstructured":"Price WN (2019) Risks and remedies for artificial intelligence in health care. The Brookings Institution, Washington, DC. https:\/\/www.brookings.edu\/articles\/risks-and-remedies-for-artificial-intelligence-in-health-care\/. Accessed 10 Sept 2025"},{"key":"12881_CR40","doi-asserted-by":"crossref","unstructured":"Filicori F, Bitner DP, Fuchs HF, Anvari M, Sankaranaraynan G, Bloom MB, Hashimoto DA, Madani A, Mascagni P, Schlachta CM, Talamini M, Meireles OR (2023) SAGES video acquisition framework-analysis of available OR recording technologies by the SAGES AI task force. Surg Endosc 37:4321\u20134327","DOI":"10.1007\/s00464-022-09825-3"},{"key":"12881_CR41","doi-asserted-by":"crossref","unstructured":"Meireles OR, Rosman G, Altieri MS, Carin L, Hager G, Madani A, Padoy N, Pugh CM, Sylla P, Ward TM, Hashimoto DA, SAGES Video Annotation for AI Working Groups (2021) SAGES consensus recommendations on an annotation framework for surgical video. Surg Endosc 35:4918\u20134929","DOI":"10.1007\/s00464-021-08578-9"},{"key":"12881_CR42","doi-asserted-by":"crossref","unstructured":"Eckhoff JA, Rosman G, Altieri MS, Speidel S, Stoyanov D, Anvari M, Meier-Hein L, M\u00e4rz K, Jannin P, Pugh C, Wagner M, Witkowski E, Shaw P, Madani A, Ban Y, Ward T, Filicori F, Padoy N, Talamini M, Meireles OR (2023) SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education). Surg Endosc 37:8690\u20138707","DOI":"10.1007\/s00464-023-10288-3"},{"key":"12881_CR43","doi-asserted-by":"crossref","unstructured":"SAGES Digital Surgery Working Group, Ali JT, Yang G, Green CA, Reed BL, Madani A, Ponsky TA, Hazey J, Rothenberg SS, Schlachta CM, Oleynikov D, Szoka N (2024) Defining digital surgery: A SAGES white paper. Surg Endosc 38:475\u2013487","DOI":"10.1007\/s00464-023-10551-7"},{"key":"12881_CR44","doi-asserted-by":"crossref","unstructured":"Maier-Hein L, Vedula SS, Speidel S, Navab N, Kikinis R, Park A, Eisenmann M, Feussner H, Forestier G, Giannarou S, Hashizume M, Katic D, Kenngott H, Kranzfelder M, Malpani A, M\u00e4rz K, Neumuth T, Padoy N, Pugh C, Schoch N, Stoyanov D, Taylor R, Wagner M, Hager GD, Jannin P (2017) Surgical data science for next-generation interventions. Nat Biomed Eng 1:691\u2013696","DOI":"10.1038\/s41551-017-0132-7"},{"key":"12881_CR45","doi-asserted-by":"crossref","unstructured":"Maier-Hein L, Eisenmann M, Sarikaya D, M\u00e4rz K, Collins T, Malpani A, Fallert J, Feussner H, Giannarou S, Mascagni P, Nakawala H, Park A, Pugh C, Stoyanov D, Vedula SS, Cleary K, Fichtinger G, Forestier G, Gibaud B, Grantcharov T, Hashizume M, Heckmann-N\u00f6tzel D, Kenngott HG, Kikinis R, M\u00fcndermann L, Navab N, Onogur S, Ro\u00df T, Sznitman R, Taylor RH, Tizabi MD, Wagner M, Hager GD, Neumuth T, Padoy N, Collins J, Gockel I, Goedeke J, Hashimoto DA, Joyeux L, Lam K, Leff DR, Madani A, Marcus HJ, Meireles O, Seitel A, Teber D, \u00dcckert F, M\u00fcller-Stich BP, Jannin P, Speidel S (2022) Surgical data science - from concepts toward clinical translation. Med Image Anal 76:102306","DOI":"10.1016\/j.media.2021.102306"},{"key":"12881_CR46","doi-asserted-by":"crossref","unstructured":"Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M, Reyes M, Riegler MA, Wiesenfarth M, Kavur AE, Sudre CH, Baumgartner M, Eisenmann M, Heckmann-N\u00f6tzel D, R\u00e4dsch T, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko MB, Cardoso MJ, Cheplygina V, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kofler F, Kopp-Schneider A, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, M\u00fcller H, Nichyporuk B, Nickel F, Petersen J, Rajpoot N, Rieke N, Saez-Rodriguez J, S\u00e1nchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, J\u00e4ger PF (2024) Metrics reloaded: recommendations for image analysis validation. Nat Methods 21:195\u2013212","DOI":"10.1038\/s41592-023-02151-z"}],"container-title":["Surgical Endoscopy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00464-026-12881-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00464-026-12881-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00464-026-12881-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:44:02Z","timestamp":1784040242000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00464-026-12881-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,8]]},"references-count":47,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["12881"],"URL":"https:\/\/doi.org\/10.1007\/s00464-026-12881-8","relation":{},"ISSN":["0930-2794","1432-2218"],"issn-type":[{"value":"0930-2794","type":"print"},{"value":"1432-2218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,8]]},"assertion":[{"value":"1 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Dr. Daniel Hashimoto is supported by the American Surgical Association Foundation Fellowship, is a consultant for Medtronic, and is a co-inventor on a patent held by Mass General Brigham related to an AI analysis system for surgical data (US Patent # 12,362,068). Dr. Hashimoto also reports prior grant funding from the Intuitive Foundation and Wellcome Leap for work outside of this manuscript. He also reports travel support to attending training courses from Intuitive and Boston Scientific. Ms. Sharon Lee and Dr. Jayson Marwaha have no conflicts of interest or financial ties to disclose. Dr. Steven Schwaitzberg reports consulting fees from Stryker Endoscopy and Kerecis, participation on Data Safety Monitoring Board as an independent medical monitor for Sealonix, board membership for Academic Insurance and SAGES Ingenuity, and stock or stock options with Levtira magnetics and HIA technologies. Ms. Mindy Duffourc reports grants from Maastricht University Medical Center, EU Horizon Project Classica, University of Miami Bucerius Law School as well as consulting fees from New York University and DDQ, B.V. Ms. Duffourc reports travel support for meeting attendance as a presenter from European Association for Endoscopic Surgery, University College Dublin, Council of Europe, University of Pisa, University of Trento, Universitat Pempeu Fabra, HIGHTECH Agenda Bavaria. Ms. Duffourc reports uncompensated leadership roles on advisory boards for Responsible AI UK Keystone Project on Addressing Socio-technical Limitations of LLMs for Medical and Social Computing, II Legal SciComm Conference 2024: Research and Practices, The Association of Coloproctology of Great Britain and Ireland (ACPGBI) AI and Data Management Taskforce Task Force on \u201cThe European Approach to Liability for the AI Age.\u201d","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosures"}},{"value":"Artificial intelligence tools, including ChatGPT 5 Pro (OpenAI), Gemini 2.5 Pro (Google), and Grammarly (Grammarly), were utilized to assist with literature search, word choice, grammar, and spelling. The authors take responsibility for all content in this publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Artificial intelligence tools"}}]}}