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Abstract

The integration of predictive modeling in dental implantology has significantly enhanced treatment planning and prognostic evaluations. This study critically examines various predictive models utilized in dental implant assessments, encompassing both conventional statistical techniques and contemporary machine learning algorithms. By systematically comparing methodological frameworks, performance metrics, and clinical applicability, this analysis aims to provide clinicians and researchers with a well-informed basis for selecting suitable predictive models tailored to specific clinical contexts