The Role of Extended Reality in Orthodontic Treatment Planning and Simulation-A scoping Review

Extended reality (XR), a nomenclature covering virtual reality (VR), augmented reality (AR), and mixed reality (MR), has emerged as a breakthrough technology in dental and medical sciences. However, the scope, effectiveness, and limitations of these technologies still remain unclear.

The aim of the present scoping review is to systematically outline the current state of XR applications for orthodontic treatment planning and simulation.

A systematic and comprehensive review of the literature was carried out in a broad array of electronic databases such as PubMed, Scopus, Google Scholar, Web of Science, Embase, IEEE Xplore, and the Cochrane Library. Studies involving the use of XR in orthodontic clinical or educational settings were included. Data extraction focused on the XR type, application purpose, target population, outcomes, and limitations. The study selection followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework.

A total of 19 studies were included, including VR (n = 13), AR (n = 5), and MR (n = 1) approaches. The identified applications demonstrate broad utility, ranging from clinical procedures such as orthodontic treatment planning, precision bracket placement, and complex surgical simulation, to pedagogical and patient-centered uses including immersive student training and interactive patient education. The most frequently reported positive outcomes across these diverse applications include enhanced spatial awareness, clinical accuracy, learner motivation and reductions in patient anxiety. Critically, the current evidence base, while promising, is constrained by predominantly small sample sizes across studies, significant methodological heterogeneity hindering meta-analysis, and a paucity of robust, long-term clinical outcome data.

XR technologies hold significant promise for transforming orthodontic care and education by enhancing treatment accuracy, clinical training, and patient engagement. However, their integration into routine practice will require high-quality, large-scale studies that establish clinical effectiveness, long-term skill retention, and cost-efficiency. Generating robust evidence, including patient-reported outcomes, is essential to bridge the gap between innovation and evidence-based implementation.