Artificial Intelligence in Caries Risk Assessment: Evaluating the Current Status of CAMBRA and Cariogram with Large Language Models

Background: Caries risk assessment (CRA) is essential for individualized caries management in early childhood, but existing tools are difficult to standardize in routine practice. The ability of large language model (LLM)–based artificial intelligence (AI) systems to recognize and correctly apply validated CRA tools remains unclear. This study aimed to evaluate the ability of ChatGPT and Google Gemini, LLM–based AI systems, to apply and interpret CRA tools in early childhood under different prompting conditions. Methods: Thirty standardized clinical vignettes of children aged 0–5 years were evaluated using Caries Management by Risk Assessment (CAMBRA) and the Cariogram by ChatGPT-5.1 (OpenAI, San Francisco, CA, USA) and Google Gemini-3.0 Pro (Google LLC, Mountain View, CA, USA) under three prompting conditions: unguided, guideline-informed, and guideline-only. An expert panel provided reference classifications. AI outputs were assessed for categorical agreement, mean absolute error (MAE), quality, accuracy, and readability. MAE differences were analyzed using the Friedman test and mixed-design repeated-measures ANOVA, while quality, accuracy, and readability were analyzed using repeated-measures analysis of variance (ANOVA). Reliability was assessed using intraclass correlation coefficients (ICC). Results: Inter- and intra-rater reliability were high (ICC = 0.89–0.93). Reference CAMBRA and Cariogram classifications showed a moderate correlation (ρ = 0.509; n = 30). MAE differed significantly across AI model–prompting condition combinations for both tools (p < 0.001). No significant difference was observed between AI models for CAMBRA, whereas ChatGPT showed lower MAE than Gemini for the Cariogram (p = 0.002). Guideline-informed and guideline-only prompting significantly reduced MAE and improved quality and accuracy compared with the unguided condition (p < 0.001). Conclusions: LLM-based AI systems can support CRA, particularly when guideline-based prompting is used. However, performance depends on the CRA tool and AI model, and numerical or algorithmic components, such as those in the Cariogram, remain challenging.