Editorial: AI and big data integration in orthopedic regenerative medicine

Orthopedic disorders such as osteoarthritis, intervertebral disc degeneration, osteoporosis, and large bone or cartilage defects represent a rising global burden in an aging society (GBD 2021 Osteoarthritis Collaborators, 2023; Hunter and Bierma-Zeinstra, 2019). 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 (Langer and Vacanti, 1993;Murphy et al., 2020). 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 (Topol, 2019;Acosta et al., 2022 ). 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 [https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2026.1768270/abstract], reflecting both the promise and the risks of large language models in medicine (Singhal et al., 2023). At the molecular level, D. Liu et al. identify shared lactylation-related gene signatures between osteoporosis and chronic kidney disease [https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1719273/full], extending orthopedic regeneration into systemic metabolic and inflammatory contexts (Zhang et al., 2019). 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.