ReviewInternational journal of dentistry2026
Integration of Digital Twin Technology in Orthodontics: A Scoping Review.
Review in International journal of dentistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Considerations in Using Artificial Intelligence to Simulate Orthodontic Treatment for Implant-Prosthetic Purposes.Journal of clinical medicine · 2026Article
- Integration of Digital Twin Technology in Orthodontics: A Scoping Review.International journal of dentistry · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Digital twin technology (DTT) is an emerging concept in healthcare enabling real-time patient and process simulations and predictive analytics that enhance personalized treatment and clinical decision-making. Orthodontics, being a comprehensive subject involving technicalities associated with diagnosis and treatment planning, the specific role of DTT, remains underexplored. This gap presents a crucial opportunity to harness its power to create dynamic virtual models for truly personalized and data-driven care. Objective: The objective is to map the extent and type of evidence available on the applicability of DTT in orthodontics, including its advantages, challenges, implementation in clinical settings, and perspective trends. Methods: A review synthesis for relevant articles published between 2005 and April 2025 in the English language was conducted across electronic databases including PubMed, Scopus, Embase, and Web of Science. Studies meeting the predefined eligibility criteria were included, and data relating to the applicability of digital twins (DTs) in orthodontics were assessed. To identify trends and inconsistencies in the existing literature, data extraction and synthesis of the findings were performed following the Joanna Briggs Institute (JBI) methodology. Results: This scoping review has collated a consolidated view of research investigating the applicability of the DTs in orthodontics specifically. Nine studies were identified that mapped applications of this technology in orthodontics, which are currently limited yet diverse, ranging from static craniofacial twins built from cone beam computed tomography (CBCT) to multimodal ones combining CBCT, intraoral scanner (IOS), and facial scans for more detailed analysis. The elaborate theoretical foundation available for this technology in other sectors has the potential to be executed in orthodontics to revolutionize the field. Conclusions: DTT exhibits immense potential and clinical significance in the field of orthodontics and dentofacial orthopedics in the nearing future to ultimately enhance the accuracy and efficiency of diagnosis and treatment like never before, improving overall experience for the clinicians, students, and the patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.