Ai Chatbots for Pediatric Fluoride Education: An Effectiveness Study

Background: Fluoride is a cornerstone of preventive pediatric dentistry, yet public concerns and online misinformation continue to undermine its acceptance. With the rise of large language model-based (LLM) chatbots, artificial intelligence (AI) tools have emerged as potential resources for delivering accessible, evidence-based health information. Objective: This study aims to evaluate the performance of three advanced AI chatbots—ChatGPT-4.o, Google Gemini Pro, and DeepSeek V3—in providing fluoride-related information to parents and caregivers, with a specific focus on pediatric dental health. Methods: Twenty frequently asked fluoride-related questions were presented to each chatbot in standardized sessions. Responses were assessed by three blinded evaluators using validated tools: EQIP, DISCERN, Global Quality Scale (GQS), Flesch Reading Ease Score (FRES), Flesch-Kincaid Reading Grade Level (FKRGL), and iThenticate similarity index. Inter-rater reliability was ensured via intraclass correlation coefficients (ICCs). Statistical analysis was performed using ANOVA or Kruskal–Wallis tests, with appropriate post-hoc methods. Results: ChatGPT-4.o outperformed the other models in EQIP and DISCERN scores (p < 0.001), indicating higher reliability and informational quality. While FRES and Similarity Index showed no significant differences, ChatGPT produced more readable and original content. All three models showed moderate variability in FKRGL and GQS outcomes. Conclusion: Among the evaluated AI chatbots, ChatGPT-4.o demonstrated superior performance in conveying fluoride-related information in a clear, reliable, and evidence-based manner. While promising as educational tools in pediatric oral health, these models should be complemented with professional oversight to ensure accuracy and appropriateness in clinical use.