The Convergence of Diabetes Care and Dental Practice: A Comprehensive Review of Artificial Intelligence Applications

Diabetes mellitus and periodontal disease share a well-established bidirectional relationship, wherein each condition adversely affects the other's progression and management. The global prevalence of both diseases necessitates innovative approaches to early detection, risk assessment, and integrated care delivery. Artificial intelligence has emerged as a transformative force in healthcare, offering unprecedented capabilities in pattern recognition, predictive analytics, and clinical decision support. This comprehensive review examines the current and potential applications of AI technologies at the intersection of diabetes and dental medicine. We explore AI-driven diagnostic tools for identifying undiagnosed diabetes during routine dental examinations, machine learning models for predicting periodontal disease progression in diabetic patients, and intelligent systems for personalizing preventive care recommendations. The review synthesizes findings from recent clinical studies and evaluates the evidence supporting various AI applications, including fuzzy logic systems for periodontitis risk assessment, support vector machines for diabetic oral ulceration prediction, and smartphone-based AI platforms for remote monitoring. Additionally, we address the challenges of clinical implementation, including data privacy concerns, algorithm validation requirements, and integration with existing electronic health record systems. Finally, we propose a framework for interdisciplinary collaboration between dental and medical professionals facilitated by AI-enabled decision support, with the goal of improving outcomes for the growing population of patients with diabetes