Integrating Stochastic Processes into Evidence-Based Dentistry: A Foundation for Precision Dentistry- A Review.

Abstract Dentistry encompasses intricate biological systems and clinical outcomes that are fundamentally variable and uncertain, attributable to variations in host response, microbial ecology, patient behavior, material characteristics, and environmental factors. Stochastic processes offer a probabilistic framework for modeling uncertainties and for elucidating the evolution of oral diseases, treatment outcomes, and dental materials over time. Stochastic modeling has been extensively utilized in dental sciences to comprehend the advancement of dental caries and periodontal diseases, forecast the durability and failure of restorations and prostheses, evaluate implant survival and peri-implant disease risk, and assess healing outcomes subsequent to endodontic and surgical interventions. Stochastic methods like Markov models, survival analysis, Poisson processes, and reliability theory also help with evidence-based clinical decision-making, risk-based treatment planning, and cost-effectiveness analysis in dental public health. Stochastic processes improve predictive accuracy and help with personalized and preventive dental care by taking into account biological variability and changes over time. In general, stochastic modeling is an important analytical tool for moving research forward, making better predictions about outcomes, and improving clinical outcomes in all areas of dentistry. Keywords: Stochastic processes; Dentistry; Markov models; Disease progression; Treatment outcome prediction; Dental public health.