Federated Learning Approaches for Privacy-Preserving Conversational AI in Dental Informatics

Abstract

This study investigates how federated learning can enable privacy-preserving conversational AI for dental clinics while complying with GDPR and HIPAA. The main objective is to determine whether clinics can automate patient communications, call handling, reminders, and triage, without transferring raw patient data beyond local systems. The methodology combines a narrative review and comparative analysis of recent healthcare federated-learning studies with a practical deployment blueprint tailored to dental workflows. Three training paradigms are contrasted, local, centralized, and federated, to summarize evidence on model accuracy and computational cost. A layered privacy stack is presented, including differential privacy, secure aggregation, and homomorphic encryption, with guidance on when each technique is most appropriate. An end-to-end workflow is described in which clinics train lightweight local adapters on anonymized speech and text, share only encrypted model updates, and receive an aggregated global model that improves across participating sites. Findings indicate that federated models achieve accuracy comparable to centralized baselines in representative tasks (e.g., segmentation and risk prediction) while keeping patient data local. Cryptographic protections introduce overhead but remain practical when applied selectively.

This study demonstrates that federated learning enables privacy-preserving conversational AI in dental informatics without compromising model performance. In practical terms, such systems can reduce missed appointments through targeted reminders, accelerate routing of urgent complaints, and support intern training with simulated dialogues, delivering the benefits of shared learning while maintaining strong confidentiality and regulatory compliance.