Artificial intelligence in pediatric dentistry: are chatbots aligned with AAPD caries risk assessment guideline?

This study evaluated the accuracy and guideline alignment of artificial intelligence (AI)–based chatbots in pediatric caries risk assessment and management by comparing their recommendations with the American Academy of Pediatric Dentistry (AAPD) caries risk assessment guideline using simulated pediatric cases. A case-based comparative study was conducted using 12 simulated pediatric patient profiles representing low, moderate, and high caries risk categories constructed according to the AAPD guideline. Five AI chatbots—ChatGPT-5.2 (Plus), ChatGPT-4o (Free), Microsoft Copilot, Google Gemini 1.5, and Claude—were evaluated. A standardized prompt instructed each chatbot to assess caries risk, recommend clinical and radiographic follow-up frequency, propose preventive interventions, and outline restorative approaches based on the AAPD guideline. Each case was presented to each chatbot on three separate days, generating 1,080 responses. Outputs were evaluated using a guideline-based scoring rubric by an experienced pediatric dentist. Statistical analyses included chi-square tests for between-chatbot comparisons and Friedman tests for within-model consistency ( p < 0.05). No significant differences were observed among the chatbots in caries risk classification ( p = 0.053). However, significant differences were found in clinical follow-up recommendations ( p = 0.013) and preventive interventions. Claude demonstrated higher accuracy in dietary counseling and fluoride therapy ( p = 0.001; p = 0.010), while Gemini and Copilot performed better in fissure sealant recommendations ( p = 0.006). No differences were observed in restorative treatment recommendations ( p = 0.480). AI chatbots were generally capable of identifying pediatric caries risk levels; however, inconsistencies were observed when translating risk status into guideline-based follow-up and preventive recommendations. AI chatbots may support dental education and preliminary clinical decision-making in pediatric dentistry, but their recommendations should be interpreted cautiously and cannot replace professional clinical judgment.

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Journal
BMC Oral Health
Published
2026-10-27
DOI
https://doi.org/10.1186/s12903-026-10135-6
Primary Topic
Dental Health and Care Utilization
Type
article
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article

Artificial intelligence in pediatric dentistry: are chatbots aligned with AAPD caries risk assessment guideline?

Zeynep Betül Arslan, ESRA CEREN TUĞUTLU
6 links
BMC Oral Health
Dental Health and Care Utilization
article

Artificial intelligence in pediatric dentistry: are chatbots aligned with AAPD caries risk assessment guideline?

Zeynep Betül Arslan, ESRA CEREN TUĞUTLU
article en

Abstract

This study evaluated the accuracy and guideline alignment of artificial intelligence (AI)–based chatbots in pediatric caries risk assessment and management by comparing their recommendations with the American Academy of Pediatric Dentistry (AAPD) caries risk assessment guideline using simulated pediatric cases. A case-based comparative study was conducted using 12 simulated pediatric patient profiles representing low, moderate, and high caries risk categories constructed according to the AAPD guideline. Five AI chatbots—ChatGPT-5.2 (Plus), ChatGPT-4o (Free), Microsoft Copilot, Google Gemini 1.5, and Claude—were evaluated. A standardized prompt instructed each chatbot to assess caries risk, recommend clinical and radiographic follow-up frequency, propose preventive interventions, and outline restorative approaches based on the AAPD guideline. Each case was presented to each chatbot on three separate days, generating 1,080 responses. Outputs were evaluated using a guideline-based scoring rubric by an experienced pediatric dentist. Statistical analyses included chi-square tests for between-chatbot comparisons and Friedman tests for within-model consistency ( p < 0.05). No significant differences were observed among the chatbots in caries risk classification ( p = 0.053). However, significant differences were found in clinical follow-up recommendations ( p = 0.013) and preventive interventions. Claude demonstrated higher accuracy in dietary counseling and fluoride therapy ( p = 0.001; p = 0.010), while Gemini and Copilot performed better in fissure sealant recommendations ( p = 0.006). No differences were observed in restorative treatment recommendations ( p = 0.480). AI chatbots were generally capable of identifying pediatric caries risk levels; however, inconsistencies were observed when translating risk status into guideline-based follow-up and preventive recommendations. AI chatbots may support dental education and preliminary clinical decision-making in pediatric dentistry, but their recommendations should be interpreted cautiously and cannot replace professional clinical judgment.

BMC Oral Health
Ankara Yıldırım Beyazıt University (TR)
Good health and well-being
Openalex Percentile: Top 10%
Dental Health and Care Utilization
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