Original ArticlesPrevalence of Machine Learning in Craniofacial SurgeryMak, Martin L.*; Al-Shaqsi, Sultan Z. MBChB, PhD†; Phillips, John MD, FRCSC†Author Information *Hospital for Sick Children †Plastic and Reconstructive Surgery, University of Toronto, Toronto, Canada. Address correspondence and reprint requests to John Phillips, MD, FRCSC, Hospital for Sick Children, 555 University Avenue, ON M5G 1X8; E-mail: email@example.com Received 17 August, 2019 Accepted 21 October, 2019 The authors report no conflicts of interest. Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jcraniofacialsurgery.com). Journal of Craniofacial Surgery: June 2020 - Volume 31 - Issue 4 - p 898-903 doi: 10.1097/SCS.0000000000006234 Buy SDC Metrics Abstract Machine learning (ML) revolves around the concept of using experience to teach computer-based programs to reliably perform specific tasks. Healthcare setting is an ideal environment for adaptation of ML applications given the multiple specific tasks that could be allocated to computer programs to perform. There have been several scoping reviews published in literature looking at the general acceptance and adaptability of surgical specialities to ML applications, but very few focusing on the application towards craniofacial surgery. This study aims to present a detailed scoping review regarding the use of ML applications in craniofacial surgery. © 2020 by Mutaz B. Habal, MD.