{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:43:28Z","timestamp":1791650608356,"version":"4.3.4"},"reference-count":105,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["World j. surg."],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) has emerged as a tool to potentially increase the efficiency and efficacy of cardiovascular care and improve clinical outcomes. This study aims to provide an overview of applications of AI in cardiac surgery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic literature search on AI applications in cardiac surgery from inception to February 2024 was conducted. Articles were then filtered based on the inclusion and exclusion criteria and the risk of bias was assessed. Key findings were then summarized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 81 studies were found that reported on AI applications in cardiac surgery. There is a rapid rise in studies since 2020. The most popular machine learning technique was random forest (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a048), followed by support vector machine (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a033), logistic regression (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a032), and eXtreme Gradient Boosting (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a031). Most of the studies were on adult patients, conducted in China, and involved procedures such as valvular surgery (24.7%), heart transplant (9.4%), coronary revascularization (11.8%), congenital heart disease surgery (3.5%), and aortic dissection repair (2.4%). Regarding evaluation outcomes, 35 studies examined the performance, 26 studies examined clinician outcomes, and 20 studies examined patient outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI was mainly used to predict complications following cardiac surgeries and improve clinicians' decision\u2010making by providing better preoperative risk assessment, stratification, and prognostication. While the application of AI in cardiac surgery has greatly progressed in the last decade, further studies need to be conducted to verify accuracy and ensure safety before use in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/wjs.12265","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T22:07:44Z","timestamp":1721254064000},"page":"2073-2089","source":"Crossref","is-referenced-by-count":20,"title":["Artificial intelligence in cardiac surgery: A systematic review"],"prefix":"10.1002","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1764-182X","authenticated-orcid":false,"given":"Ralf Martz","family":"Sulague","sequence":"first","affiliation":[{"name":"Graduate School of Arts and Sciences Georgetown University  Washington District of Columbia USA"},{"name":"Smidt Heart Institute Cedars\u2010Sinai Medical Center  Los Angeles California 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