Abstract
Removable partial dentures (RPDs) remain a widely used and cost-effective solution for patients with dentition defects. However, their long-term success, particularly in distal extension cases, depends heavily on biomechanical performance. Finite element analysis (FEA) has emerged as a valuable tool for evaluating stress distribution and guiding RPD design. This review synthesizes FEA-based insights into key biomechanical parameters-including abutment selection, clasp geometry, rest position, major connector stiffness, and material properties-with a particular focus on Kennedy Class I and II scenarios, and special attention to implant-supported RPDs (ISRPDs). Recent developments in digital workflows, such as intraoral scanning and CAD/CAM fabrication, have further enabled personalized modeling and rapid optimization. In addition, the integration of artificial intelligence (AI) with FEA shows promises in automating framework generation, predicting stress outcomes, and supporting closed-loop design optimization. While these technologies offer exciting potential, current models still lack integration of patient-specific factors such as mucosal properties, saliva, and gag reflex, contributing to discrepancies between simulations and clinical outcomes. Bridging this gap through improved modeling and data-driven approaches will be key to delivering personalized, biomechanically optimized RPD solutions.