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This narrative review examines how knee biomechanical simulation has progressed from conventional FEA and efficient physics-based approximations to contact emulators, hybrid FE\u2013machine 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.<\/jats:p>","DOI":"10.1177\/09544119261477934","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T17:25:59Z","timestamp":1787246759000},"page":"879-893","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward foundation-model-assisted biomechanical simulation of the knee joint: Current evidence, limitations, and future directions"],"prefix":"10.1177","volume":"240","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0058-6051","authenticated-orcid":false,"given":"Zhengye","family":"Pan","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department, Institute of Advanced Clinical Medicine, Peking University, Beijing, China"},{"name":"Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Luo","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, Institute of Advanced Clinical Medicine, Peking University, Beijing, China"},{"name":"Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"e_1_3_2_2_2","article-title":"Global, regional, and national burden of osteoarthritis, 1990\u20132020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021","volume":"5","author":"GBD 2021 Osteoarthritis Collaborators","year":"2023","unstructured":"GBD 2021 Osteoarthritis Collaborators. 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