{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T13:38:19Z","timestamp":1793367499822,"version":"4.5.0"},"reference-count":44,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"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":["J Orthod"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:sec><jats:title>Background:<\/jats:title><jats:p> The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed. <\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives:<\/jats:title><jats:p> To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT. <\/jats:p><\/jats:sec><jats:sec><jats:title>Design:<\/jats:title><jats:p> Systematic review. <\/jats:p><\/jats:sec><jats:sec><jats:title>Data sources:<\/jats:title><jats:p> Unrestricted search of indexed databases and reference lists of included studies. <\/jats:p><\/jats:sec><jats:sec><jats:title>Data selection:<\/jats:title><jats:p> Clinical studies that addressed the focused question \u2018Is AI useful for treatment planning and soft tissue outcome prediction in OGT?\u2019 were included. <\/jats:p><\/jats:sec><jats:sec><jats:title>Data extraction:<\/jats:title><jats:p> Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration\u2019s RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively. <\/jats:p><\/jats:sec><jats:sec><jats:title>Data synthesis:<\/jats:title><jats:p> Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB. <\/jats:p><\/jats:sec><jats:sec><jats:title>Limitations:<\/jats:title><jats:p> Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion:<\/jats:title><jats:p> AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT. <\/jats:p><\/jats:sec><jats:sec><jats:title>Registration:<\/jats:title><jats:p> PROSPERO CRD42022366864. <\/jats:p><\/jats:sec>","DOI":"10.1177\/14653125231203743","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T10:02:21Z","timestamp":1695981741000},"page":"107-119","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":28,"title":["Artificial intelligence for 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