BMC Oral Health· 2026Q1
Artificial intelligence in pediatric dentistry: are chatbots aligned with AAPD caries risk assessment guideline?
- 0citations
- Q1SCImago
- 2026year
Short summary
AI chatbots generally identify pediatric caries risk levels but show significant inconsistencies in translating risk into guideline-based follow-up and preventive recommendations, according to a study comparing five AI models against AAPD guidelines using simulated cases.
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Key points
- Five AI chatbots were tested against AAPD caries risk assessment guidelines using 12 simulated pediatric cases.
- Chatbots showed general accuracy in identifying caries risk levels (p=0.053).
- Significant inconsistencies were found in chatbot recommendations for clinical follow-ups (p=0.013) and preventive interventions.
- Claude performed better in dietary counseling and fluoride therapy (p=0.001; p=0.010), while Gemini and Copilot led in fissure sealant recommendations (p=0.006).
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. BMC Oral Health, 2026 · DOI ↗
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Field: Periodontics
PeriodonticsDentistry