10.1177/1056789514562152 (CrossRef Listing of Deleted DOIs, 2015)
Subject-specific finite element analysis (FEA) can estimate tissue-level stress, strain, contact pressure, and load transfer in the knee, but building and solving a new model for each anatomy, activity, or treatment scenario remains time-consuming. This narrative review examines how knee biomechanical simulation has progressed from conventional FEA and efficient physics-based approximations to contact emulators, hybrid FE-machine learning workflows, temporal models, geometric deep learning, graph neural networks, operator-learning methods, and physics-guided surrogates. These methods can accelerate selected predictions, although their accuracy is usually established only for the anatomies, loads, and FE formulations represented during development. Pre-training across related FE model families may allow a model to be adapted to new geometries, outputs, or tasks with fewer additional simulations. However, this has not yet been demonstrated for nonlinear, multi-tissue knee mechanics. Any such model would still inherit the constitutive laws, contact definitions, boundary conditions, and calibration choices used to generate its training data. A credible system would therefore need traceable simulation data, explicit information about the underlying FE formulation, reliable uncertainty estimates, mechanical checks, and confirmation with high-fidelity FEA when a case falls outside the supported range. The immediate goal is a model that can be reused across related FE problems without being retrained from scratch, while unsupported or clinically consequential cases remain subject to full FE analysis.