AI-Augmented Teach-Back in Dentistry: From Patient Education to Verified Clinical Understanding

Oral diseases remain among the most prevalent chronic conditions worldwide, yet their prevention and long-term management depend heavily on patient understanding of self-care, maintenance protocols, and procedural risks. Existing educational programs have not solved this problem because most individuals do not understand dental health, and communication with patients remains poor, which results in poor treatment outcomes. The teach-back method functions as an evidence-based communication tool that helps staff members confirm that patients have grasped essential information, but dental practitioners use it inconsistently because it requires manual work and produces no measurable biological results. This review presents artificial intelligence (AI)-augmented teach-back as a communication system that unites dental evidence with healthcare field evidence to create a scalable system that maintains equity and enables verification. The review uses conceptual synthesis based on implementation science together with digital health and clinical communication research to study teach-back applications in pediatric, preventive, periodontal, surgical, geriatric, tele-dental, and public-health dentistry, which face three main challenges: standardization, workflow burden, and outcome measurement. Advances in natural language processing, speech recognition, semantic understanding scoring, explainable AI (XAI), and risk-adaptive communication systems have enabled teach-back to evolve from its original form as a recall-based educational method into a clinical process that can be audited and produces long-term results. This review uses three established frameworks together with the SALIENT AI-specific governance model to evaluate organisation readiness for implementation, their ethical safeguards, and their methods for achieving equity. The system provides three main advantages, including its ability to combine information from various fields, its design to match actual operational procedures, and its direct connection between communication verification methods and biological measurement results. This study depends on indirect evidence that comes from non-dental environments but does not include future dental research studies. The review presents communication as a clinical risk that can be modified and demonstrates how AI-augmented teach-back functions as a ready-to-use system for dental care improvement through better patient adherence and enhanced safety and equity distribution.