Medical Science: Updates and Prospects Vol. 12

Digital twin technology is an emerging approach for integrating patient-specific data into dynamic virtual models that can support diagnosis, treatment planning, simulation, and monitoring in healthcare. In endodontics, its relevance arises from the complex anatomy of the root canal system, the need for accurate three-dimensional assessment, and the growing use of artificial intelligence and computational modelling in clinical decision-making. This chapter reviews the potential contribution of digital twins to personalised and predictive root canal therapy. It considers the main technological components required for their development, including cone-beam computed tomography, intraoral scanning, digital radiography, three-dimensional reconstruction, artificial intelligence, machine learning, computational modelling, and secure data integration. It also discusses how patient-specific virtual replicas may support endodontic diagnosis, guided procedures, instrumentation simulation, irrigation modelling, obturation assessment, restorative planning, follow-up, and outcome prediction. In addition, digital twins may contribute to endodontic education through virtual patients, simulation-based learning, objective performance assessment, and personalised training pathways. However, implementation remains limited by challenges related to data quality, interoperability, computational demands, cybersecurity, ethical and legal considerations, regulatory requirements, and insufficient clinical validation. Digital twins should therefore be regarded as promising but developing tools that require validation, governance, and careful integration into clinical workflows. With careful implementation, they may contribute to more individualised and evidence-informed endodontic care.