Orthopedic disorders such as osteoarthritis, intervertebral disc degeneration, osteoporosis, and large bone or cartilage defects represent a rising global burden in an aging society (; ). Regenerative medicine and tissue engineering offer transformative strategies, yet clinical translation remains constrained by biological heterogeneity, fragmented evidence, and the inherent complexity of biomaterial development (; ). Artificial intelligence (AI) and big data are now reshaping this landscape, evolving from auxiliary analytic tools into a connective infrastructure that integrates mechanism, material, and medicine (; ). This Research Topic brings together eight contributions that collectively envision how AI may accelerate the next phase of orthopedic regeneration.
A first conceptual axis concerns AI-enabled design and translation. Xu et al. delineate how AI supports biomaterial screening, tissue construct design, surgical assistance, and post-operative assessment across the translational chain. Building on this view, Liu M. et al. describe AI-driven biomaterial design as an intelligent closed loop linking reverse design, performance prediction, and biological response. Such paradigms mirror broader advances in materials informatics and autonomous experimentation, where computational prediction and high-throughput validation are progressively unified (; ). The implication for orthopedics is significant: future scaffolds, hydrogels, and combination products may be designed within iterative AI–experiment loops rather than through empirical optimization alone.
A second axis addresses intervertebral disc repair, a mechanically and biochemically demanding regenerative target (). Wang et al. reconstruct the evidence architecture of hydrogel-based disc repair through AI-assisted mapping, clarifying knowledge clusters and unresolved gaps. Shi and Wang complement this with an opinion proposing an AI-driven blueprint to decode disc repair mechanisms and guide intelligent biomaterial design. Together, they suggest that future disc regeneration strategies will rely on the convergence of literature-scale evidence synthesis, mechanistic modeling, and computationally informed material engineering.
A third axis concerns multimodal data and knowledge integration. Ding et al. review how AI bridges imaging, omics, and clinical data to enable precise stratification and individualized regenerative strategies—a trajectory consistent with the broader rise of multimodal biomedical AI (). Li et al. apply AI- and big data-driven knowledge mapping to exosome–hydrogel research, revealing emerging hotspots at the interface of cell-free therapy and biomaterials (). These contributions highlight that future orthopedic regeneration will increasingly depend on the orchestration of heterogeneous data—from molecules to patient outcomes.
A fourth axis explores AI for clinical reasoning and mechanism discovery. Xiao et al. benchmark conversational AI systems for evidence-based decision-making in cartilage repair, reflecting both the promise and the risks of large language models in medicine (). At the molecular level, Liu D. et al. identify shared lactylation-related gene signatures between osteoporosis and chronic kidney disease, extending orthopedic regeneration into systemic metabolic and inflammatory contexts (). Together, these works signal a future in which AI not only informs material design but also reshapes clinical decisions and reveals previously hidden disease axes.
Looking forward, several priorities emerge. First, the field requires standardized, multi-center, and longitudinal datasets that can support robust model training and external validation. Second, interpretable and mechanistically grounded AI will be essential, particularly when models guide biomaterial selection or clinical interventions. Third, computational predictions must be coupled with prospective experimental and clinical validation to ensure translational reliability. Fourth, ethical, regulatory, and human-factor considerations must evolve in parallel with technical capability, especially as generative and agentic AI systems enter clinical workflows. Finally, the most transformative advances will likely emerge from genuinely interdisciplinary teams uniting orthopedic surgeons, stem cell biologists, materials scientists, bioinformaticians, engineers, and data scientists.
AI should not be regarded as a replacement for biological insight or clinical judgment, but as a connective infrastructure that converts complex data into testable hypotheses, optimized biomaterials, personalized interventions, and measurable patient outcomes. The contributions assembled here outline an emerging blueprint for this convergence and point toward a future in which orthopedic regenerative medicine is increasingly data-informed, mechanism-aware, and translationally accelerated. We hope this Research Topic catalyzes further interdisciplinary innovation and supports the responsible integration of AI and big data technologies into the next-generation of orthopedic care.
Statements
Author contributions
ZYa: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing. ZYu: Conceptualization, Data curation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft. XW: Conceptualization, Investigation, Supervision, Validation, Writing – review and editing. JL: Investigation, Supervision, Validation, Writing – review and editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This work was funded by Peking University People’s Hospital Scientific Research Development Funds (RDX2025‐04), and the Research Project on Innovation and Development of Young Scientific and Technological Talents in Orthopedics (2025) (GSKQNJJ-2025-001).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
artificial intelligence, big data, biomaterials, clinical translation, multimodal data integration, orthopedics, regenerative medicine, tissue engineering
Citation
Yang Z, Yuan Z, Wu X and Lin J (2026) Editorial: AI and big data integration in orthopedic regenerative medicine. Front. Cell Dev. Biol. 14:1892774. doi: 10.3389/fcell.2026.1892774
Received
27 May 2026
Accepted
01 June 2026
Published
10 June 2026
Volume
14 - 2026
Edited and reviewed by
Valerie Kouskoff, The University of Manchester, United Kingdom
Updates
Copyright
© 2026 Yang, Yuan, Wu and Lin.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhen Yang, zhenyang_pku@163.com; Jianjing Lin, linjianjing@bjmu.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.