Edge computing, Internet of Things and cybersecurity in the deployment of dental artificial intelligence

The deployment of dental artificial intelligence depends not only on the accuracy of the models but on the infrastructure that hosts them and the security that protects them, and this narrative review synthesizes the evidence on edge computing, the Internet of Things and cybersecurity as the three determinants of that deployment. The available evidence converges on the conclusion that edge computing reduces latency and preserves privacy but constrains model size, that the Internet of Things extends data collection into the home and the mouth while multiplying the attack surface, and that cybersecurity is the least mature of the three, with adversarial attacks and data governance emerging as the principal risks. The synthesis integrates the dental-specific evidence with the wider edge-computing and medical-device-security literature, and it identifies the hybrid local-cloud architecture and federated learning as the approaches that best reconcile privacy with performance. The review concludes that the technical infrastructure for dental artificial intelligence is maturing faster than the security and governance that its deployment requires, and that the field must close that gap before connected dental intelligence can be deployed at scale.