Data-driven precision diagnosis of periodontal disease: from biological features analysis to clinical applications

Periodontal disease is a prevalent chronic inflammatory condition, and its heterogeneity and complex pathophysiology pose significant diagnostic challenges. Traditional methods remain limited in precision and early detection. With the advancement of precision medicine, data-driven diagnostics are shifting periodontal assessment from a “one-size-fits-all” model to a more precise approach. Moreover, numerous studies highlight the vast potential of artificial intelligence (AI) in periodontal risk assessment and diagnosis. Integrating AI with data-driven diagnostic models enables deeper analysis, potentially surpassing the limitations of conventional empirical medicine and establishing a new paradigm for precision oral healthcare. This review explores recent advances in data-driven and AI-based strategies for periodontal disease diagnosis, emphasizing the multidimensional integration of biological features analysis and providing new insights into periodontal precision diagnostics.