Artificial intelligence virtual bone organoids (AIVBOs)

Global population aging has made skeletal disorders a major threat to human health. Solid bone organoids reproduce skeletal physiology and pathology more faithfully than traditional models, yet remain limited in standardisation, real-time monitoring, and high-throughput use. Integrating artificial intelligence (AI) with computational biology offers a route to ease these limits and motivates an emerging direction we develop here. We use virtual bone organoids (VBOs) for the in silico counterpart of a solid bone organoid, and artificial intelligence virtual bone organoids (AIVBOs) for its AI-driven realisation, which takes the virtual cell as its basic unit and fuses multimodal omics data with biophysical constraints. It remains an early-stage proposal, since most capabilities discussed here have been shown only in non-bone systems such as virtual cells and AI-assisted organoids and are carried over to bone rather than established in it. This review traces the logic from solid bone organoids to the AIVBO as a meso-scale digital-twin approach. We outline a modular strategy across stem-cell differentiation, vascular and immune coupling, and pathological evolution, and consider the prospective value of AIVBOs for standardised organoid construction, reverse design of biomaterials, mechanistic analysis, reagent-sparing drug screening, and in silico trials. Throughout, we separate goals already supported by real data from longer-term aspirations and address the main limitations directly, including data noise, bias, and sparsity, organoid irreproducibility, limited digital-twin fidelity, interpretability, data fusion, and ethical and regulatory issues. As a digital bridge between microscopic mechanism and macroscopic phenotype, AIVBOs may help move bone research from descriptive experimentation toward predictive engineering, though substantial experimental validation remains a prerequisite for clinical translation.

AIVBOs are framed as a meso-scale digital-twin concept meant to complement, not replace, solid bone organoids and in vivo models. Their translational potential lies in using calibrated in silico models to narrow the experimental search space for biomaterial design, drug screening, and disease modelling, which could cut the cost and time of pre-clinical orthopaedic research and support more standardised, predictive, and personalised care for bone repair and metabolic bone disease. These benefits are prospective and contingent on rigorous validation, transparent reporting of model limits, and shared data and quality standards.