Emotion-Sensitive Artificial Intelligence for Behavioral Response Prediction in Pediatric Dental Patients

Abstract

Pediatric dental patients often experience anxiety and fear, which can negatively impact their cooperation and treatmentoutcomes. This study explores the use of emotion-sensitive artificial intelligence (AI) to predict behavioral responses inchildren during dental procedures. By analyzing multimodal data—including facial expressions, vocal cues, and physiologicalsignals—an AI model is developed to identify emotional states and forecast likely behaviors. The model’s predictionsare validated against clinician observations to assess accuracy and reliability. Results indicate that emotion-sensitive AIcan effectively anticipate behavioral responses, enabling personalized intervention strategies that improve patient comfortand procedural efficiency. This approach offers a promising framework for integrating real-time emotional intelligence intopediatric dental care, enhancing both patient experience and clinical outcomes.