Comparative Evaluation of Biomechanical Parameters of Tissues After Aesthetic Rehabilitation Using Generative Neural Networks and Standard Planning Protocols

Objective: The current advancements in digital workflows and artificial intelligence in estimating tissue overload, restoration failures, and longevity predictions have increased the biomechanical precision in the field of esthetic dental rehabilitation and aim to analyze and compare the biomechanical parameters of aesthetic dental rehabilitation using generative neural networks versus traditional clinician-driven methods. Methodology: A narrative review from 2015 to 2025 examined the PubMed, Scopus, Web of Science, IEEE Xplore, and Cochrane library for studies in English concerning finite element, laboratory, clinical, and AI studies that include the outcomes of biomechanics. Results: GNN assisted planning showed significant biomechanical gains in intricate rehabilitations, especially in multi-unit and implant-supported restorations. Declined peak stresses and more uniform distribution in peri-implant and periodontal structures, improved control of deformations, and thorough reconstruction in more optimized geometries, especially in the stresses or deformations, were noted. The evidence is mostly simulation-based, methodologically heterogeneous, and lacks thorough and sustained clinical validation. Conclusions: GNN-based planning indicates possible biomechanical advantages, notably less peak stress for complicated dental rehabilitations; however, most evidence is simulation-based. The GNN workflow's clinical implementation requires biomechanical principles and relies on predictive analysis, explainable AI, and cross-disciplinary substantiation.