Publication:

The 4D virtual patient in digital dentistry: can multimodal data integration and specialty-specific applications pave the road toward dental digital twins?

The virtual patient in dentistry represents an integrated digital model rather than a single imaging modality or isolated digital workflow. This structured narrative review examined the principal components, clinical applications, and translational limitations of virtual-patient approaches in dentistry, including intraoral scanning, cone-beam computed tomography, facial scanning, digital photography, jaw-motion tracking, virtual articulation, occlusal analysis, and emerging digital-twin concepts. A literature search of PubMed, Scopus, and Web of Science was conducted, and the evidence was analyzed comparatively according to data source, integration method, specialty-specific application, clinical purpose, and current evidence maturity across periodontics, implant dentistry, prosthodontics, orthodontics, endodontics, pediatric dentistry, and oral and maxillofacial surgery. Overall, the findings suggest that the virtual patient does not represent a uniform level of clinical maturity across dental disciplines. Prosthodontics, implant dentistry, orthodontics, and oral and maxillofacial surgery currently show the strongest clinically anchored applications, particularly for facially driven treatment planning, prosthetically guided implant placement, digital setups, orthognathic simulation, and surgical transfer. By contrast, applications in periodontics, endodontics, and pediatric dentistry remain more focused on diagnosis, monitoring, guided precision, and longitudinal documentation rather than fully predictive digital modelling. Current limitations include registration error, scanner-dependent accuracy, soft-tissue distortion, CBCT artifacts, incomplete dynamic occlusal simulation, software interoperability barriers, and limited evidence linking virtual-patient workflows to long-term clinical outcomes. Future progress will depend on standardized validation studies that assess accuracy, transfer reliability, clinical efficiency, patient communication, and patient-relevant outcomes, while clearly distinguishing virtual-patient modelling from the more ambitious concept of a true dental digital twin.