Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings

Objective: To develop a reproducible generative artificial intelligence (GenAI)-driven workflow for periodontitis risk stratification using systemic and demographic indicators, and to validate its ability to identify well-established predictors in resource-limited settings. Materials and methods: This retrospective study analyzed data from 416 dental hospital patients. Using systematic prompt engineering, GenAIwas employed to automate data preprocessing, correlation analysis, and development of six machine learning models (namely, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, and K-Nearest Neighbors) to predict periodontitis severity. Severe periodontitis was defined as Community Periodontal Index (CPI) score of 4. Model validation was performed using an 80-20 data split and fivefold cross-validation and McNemar's Test. Results: The GenAI-driven pipeline successfully automated the data analysis workflow. Models achieved modest discriminatory power using systemic indicators alone (AUC 0.48-0.57). Logistic Regression demonstrated the most balanced performance (72% accuracy, 74% F1-score), while Support Vector Machine (SVM) showed superior sensitivity (89%) for screening severe cases. Feature importance analysis identified age (score = 0.233) and blood sugar level (score = 0.209) as the strongest predictors, consistent with established periodontal risk factors. Notably, composite systemic risk scores exhibited a stronger correlation with periodontitis severity than any individual health parameter. Conclusion: While systemic indicators alone provided limited diagnostic precision, the GenAI-driven workflow effectively automated data process with end-to-end model development. The high sensitivity of the SVM model suggests potential utility as a preliminary screening tool to flag at-risk individuals for prioritized clinical examination, particularly in settings where dental radiography is unavailable. Clinical relevance: This research demonstrates the potential of GenAI to facilitate efficient and interpretable risk stratification rather than definitive diagnosis. The workflow provides a replicable, privacy-preserving framework that lowers the technical barrier to applied machine learning in resource-limited periodontal care.