Predictive, Preventive and Personalized Medicine Approaches in Periodontitis: Emerging Technologies from Biomarkers to Artificial Intelligence–Driven Integrated Strategies
EPMA Journal, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s13167-026-00458-3
- Dergi Adı: EPMA Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Artificial intelligence, Digital health monitoring, Multimodal diagnostics, Oral microbiome, Periodontitis, Predictive preventive personalized medicine (PPPM / 3PM), Salivary biomarkers, Smart dental devices
- Erciyes Üniversitesi Adresli: Evet
Özet
Rationale and purpose: Periodontitis is a chronic multifactorial inflammatory disease linked to systemic conditions including cardiovascular disease and diabetes, underscoring the need for a holistic diagnostic approach. Current diagnostics rely on clinical probing and radiography, which detect accumulated damage rather than early biological activity or individual progression risk. This reactive paradigm delays intervention and limits personalization. Within predictive, preventive and personalized medicine (PPPM), this review examines emerging diagnostic technologies and evaluates their potential to enable a paradigm shift toward early prediction, targeted prevention, and individualized periodontal management. Working hypothesis: The convergence of molecular biomarkers, advanced imaging, microbiome profiling, artificial intelligence, and smart oral technologies into a multimodal framework can support the transition from reactive care to PPPM by enabling detection of suboptimal health states before irreversible damage, continuous digital health monitoring beyond episodic visits, and AI-driven patient stratification for individualized protection against health-to-disease transition and disease progression. Key findings and data interpretation: Salivary and gingival crevicular fluid biomarkers detect inflammatory activity prior to clinical attachment loss, supporting early risk identification. Optical coherence tomography and Raman spectroscopy capture structural and biochemical tissue changes non-invasively. Next-generation sequencing reveals early dysbiotic shifts preceding clinical deterioration. Artificial intelligence integrates these heterogeneous datasets into patient-specific risk signatures, with recent PPPM-oriented studies confirming feasibility of automated oral health assessment. Smart oral devices extend monitoring into daily life, enabling continuous surveillance of behavioral and biochemical risk parameters. Conclusions and expert recommendations in the framework of PPPM: For predictive diagnostics, biomarker panels and AI-driven analysis enable identification of preclinical disease activity and individual risk stratification. For targeted prevention, digital monitoring and wearable technologies support continuous risk surveillance and timely individualized interventions. For personalization of medical services, multimodal data integration through AI facilitates patient-specific treatment planning and adaptive care pathways. This integrative framework goes beyond technology-focused reviews by positioning emerging periodontal diagnostics within a unified PPPM paradigm, contributing to the shift from reactive care toward predictive, preventive and personalized disease management.