{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T20:37:53Z","timestamp":1785789473665,"version":"3.56.0"},"reference-count":62,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Global Spine Journal"],"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:sec>\n                    <jats:title>Study Design:<\/jats:title>\n                    <jats:p>Narrative review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies with the potential to drastically affect clinical decision making in spine surgery. AI can enhance the delivery of spine care in several arenas: (1) preoperative patient workup, patient selection, and outcome prediction; (2) quality and reproducibility of spine research; (3) perioperative surgical assistance and data tracking optimization; and (4) intraoperative surgical performance. The purpose of this narrative review is to concisely assemble, analyze, and discuss current trends and applications of AI and ML in conventional and robotic-assisted spine surgery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2019 examining AI, ML, and robotics in spine surgery. Key findings were then compiled and summarized in this review.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The majority of the published AI literature in spine surgery has focused on predictive analytics and supervised image recognition for radiographic diagnosis. Several investigators have studied the use of AI\/ML in the perioperative setting in small patient cohorts; pivotal trials are still pending.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Artificial intelligence has tremendous potential in revolutionizing comprehensive spine care. Evidence-based, predictive analytics can help surgeons improve preoperative patient selection, surgical indications, and individualized postoperative care. Robotic-assisted surgery, while still in early stages of development, has the potential to reduce surgeon fatigue and improve technical precision.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/2192568220915718","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T05:57:05Z","timestamp":1585720625000},"page":"556-564","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":107,"title":["Artificial Intelligence and Robotics in Spine Surgery"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5085-8422","authenticated-orcid":false,"given":"Jonathan J.","family":"Rasouli","sequence":"first","affiliation":[{"name":"Cleveland Clinic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianning","family":"Shao","sequence":"additional","affiliation":[{"name":"Cleveland Clinic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sean","family":"Neifert","sequence":"additional","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wende N.","family":"Gibbs","sequence":"additional","affiliation":[{"name":"Mayo Clinic Scottsdale"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghaith","family":"Habboub","sequence":"additional","affiliation":[{"name":"Cleveland Clinic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael P.","family":"Steinmetz","sequence":"additional","affiliation":[{"name":"Cleveland Clinic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Benzel","sequence":"additional","affiliation":[{"name":"Cleveland Clinic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas E.","family":"Mroz","sequence":"additional","affiliation":[{"name":"Cleveland Clinic"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2020,4]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0000000000002974"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/jsp2.1044"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.nec.2019.02.012"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/neuros\/nyy630"},{"key":"e_1_3_2_6_2","first-page":"1","article-title":"Predicting nonroutine discharge after elective spine surgery: external validation of machine learning algorithms","volume":"26","author":"Stopa BM","year":"2019","unstructured":"Stopa BM, Robertson F, Karhade AV, et al. 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