Federated learning for patient data privacy in dental artificial intelligence

Dental artificial intelligence needs large, diverse datasets to generalize, but the records that would form those datasets are protected by privacy law and are held in scattered clinics. Federated learning trains a shared model without moving the data, and it is the most promising answer to that tension, but its evidence base in dentistry is young. This applied review synthesizes the evidence on federated learning for dental artificial intelligence, ordered by practical relevance, and states concrete implications for practitioners. The evidence shows that federated learning reaches accuracy close to centralized training for caries detection, panoramic pathology detection and oral cancer screening, and that it can be combined with differential privacy and interpretability at a modest cost. In practice, a dental network that wants to pool its data without pooling its data should start with a federated caries or pathology detector, because those tasks are the ones the evidence supports most strongly. For regulators, the evidence implies that federated learning is not a substitute for legal compliance but a technical complement. The field still lacks a dental benchmark and long-term deployment studies, and each gap is paired with a concrete recommendation.