Digital Twins in Dentistry: Technological Foundations and Clinical Applications

Abstract

Digital  twins have become a powerful new trend in dentistry, enabled by advancements in multimodality imaging, artificial intelligence (AI), and numerical simulation. Dental digital twins are living, data-driven virtual counterparts to a patient’s oral and craniofacial anatomy that allow real-time modeling, prediction, and optimization of clinical treatment. This review aimed to gather current knowledge about technical concepts, clinical fields, limitations, and future challenges of digital twins in dentistry. These key enabling technologies (including cone-beam computed tomography, intraoral scanning, facial surface imaging, and deep learning-based segmentation) contribute to accurate anatomical reconstruction and the generation of automated models. From a clinical perspective, digital twins appear particularly promising for implant planning, simulation of orthodontic treatment and orthognathic surgery, and patient communication. Research suggests that this technology may improve accuracy, workflow efficiency, and patient involvement. Many challenges, however, persist in regard to insufficient clinical validation; poor interoperability; high infrastructure requirements and authority issues in data access use cases . New modalities such as Internet of Things (IoT)-driven monitoring, blockchain-verified data sharing, and the integration of genomic information with biomechanical models may expand the range of applications for dental digital twins, enabling precision dentistry. Despite significant progress, digital twins are a developing technology. Scalability for clinical use will require robust evidence, standardized protocols, regulatory guidance, and ethical and societal considerations. Digital twins have significant potential to transform practice, education, and research in dentistry, but their benefits could be widespread only with responsible development and broad clinical evaluation.