Integrating Oral and General Health Epidemiology Through Data Science and AI

Oral health is often separated from systemic health in both epidemiological research and healthcare delivery, despite growing evidence linking oral conditions such as periodontitis, dental caries, and oral infections with cardiovascular, metabolic, and inflammatory diseases. As healthcare systems increasingly recognize the interconnected nature of human health, there is rising interest in approaches that integrate oral and systemic health research within broader public health and clinical frameworks. The emergence of AI, big data analytics, and digital epidemiology creates new opportunities to better understand complex health relationships through large-scale data analysis, predictive modeling, and intelligent healthcare systems. These technologies identify patterns, improve early detection, and support more personalized and preventive approaches to care. Together, these developments reshape how oral and systemic health are studied, monitored, and addressed within modern healthcare and epidemiological research. Integrating Oral and General Health Epidemiology Through Data Science and AI: EpiDentomics introduces an integrative paradigm that redefines the scope of modern epidemiology by combining the analytical rigor of data science with the contextual insights of population health. Through real-world data, AI-driven predictive models, and global burden analyses, this book reveals how oral-systemic interrelationships can guide personalized prevention, risk stratification, and precision public health strategies. Covering topics such as endodontic and restorative diseases, image-based diagnostics, and predictive modeling of oral health behaviors, this book is an indispensable academic resource for graduate and doctoral students, dental researchers, oral medicine specialists, clinical dentists, data scientists, biostatisticians, health informatics specialists, biomedical engineers, policymakers, and more.