Artificial intelligence-enabled virtual organoids: Digital-twin frameworks for predictive stem cell biology and precision regenerative medicine

Stem cell and organoid technologies have transformed the study of human development, disease, and tissue regeneration, yet their broader scientific and clinical utility remains constrained by experimental variability, limited throughput, destructive endpoint measurements, incomplete longitudinal tracking, and the difficulty of systematically interrogating complex cell-cell and cell-microenvironment interactions.Recent advances in artificial intelligence, multimodal single-cell profiling, high-content imaging, biosensing, and mechanistic simulation are creating the foundations for a new computational paradigm: Artificial intelligence-enabled virtual organoids (AIVOs).We define AIVOs as dynamically updated, biologically constrained digital representations of stem cell-derived organoid systems that integrate experimental data with predictive models to simulate cell-state transitions, tissue self-organisation, perturbation responses, and therapeutic outcomes.Rather than static computational replicas, AIVOs are envisioned as executable digital twins in which virtual cells represent cellular interactions underlying emergent tissue properties.Their proposed architecture comprises five linked stages: Longitudinal multimodal observation, dynamic internal-state estimation, executable virtual-cell and hybrid multiscale modelling, prediction and virtual perturbation, and experimental validation with iterative updating.Longitudinal multimodal measurements provide temporally resolved evidence for estimating cellular and tissue states.These states can be represented in hybrid mechanistic and data-driven models to support trajectory prediction and virtual perturbations.However, no complete AIVO satisfying all of these criteria has yet been demonstrated.Available studies instead largely represent enabling technologies or methodological precedents, with only limited evidence for partial or early AIVO prototypes.Potential applications span developmental biology, disease modelling, precision medicine, and regenerative medicine.AIVOs may prospectively support investigation of lineage and tissue patterning, representation of patient-specific heterogeneity and disease dynamics, virtual evaluation of treatment 4 / 74 strategies, and optimisation of differentiation, biomaterial, and graft-design parameters.However, the realisation of AIVOs will require substantial methodological and conceptual advances.Key priorities include standardised longitudinal datasets, interpretable modelling, uncertainty quantification, causal validation, multicentre benchmarking, and assessment of transferability across donors, laboratories, and platforms.Regulatory evaluation will also need to distinguish exploratory simulation from clinically actionable prediction and to define evidentiary standards for digital-twinsupported decision-making.AIVOs may thereby shift organoid research towards more predictive, testable, and adaptive modelling across stem cell biology, disease modelling, drug development, and regenerative medicine.