Artificial Intelligence Virtual Organoids (AIVOs)
Long Bai, Jiacan Su
Abstract
Organoid platforms have reshaped in vitro human biology yet remain constrained by batch variability, sparse longitudinal readouts and barriers to scale. This review introduces Artificial Intelligence Virtual Organoids (AIVOs), also termed silicon organoids: organoid-scale digital twins instantiated in the computational space, with virtual cells-and, where appropriate, virtual organoids-serving as the minimal executable units. AIVOs fuse multimodal and longitudinal measurements into universal state representations and use virtual instruments constrained by biophysical priors to emulate assays and perturbations, while hybrid mechanistic modules (agent-based, continuum, finite-element) capture cell-cell, cell-matrix and transport dynamics. The article defines conceptual boundaries, formalizes a data-model-interaction architecture and construction strategies, and synthesizes evaluation and standardization practices. Applications span drug screening and dosing design, disease subtyping and resistance mapping, integration with organoid-on-chip systems and clinical decision support. Principal challenges include the acquisition and harmonization of high-quality longitudinal data, scalable computation and model reduction, interpretability and causal reasoning, and governance addressing privacy, safety and fairness. Virtual organoids ultimately provide a silicon-grounded, transparent and reproducible bridge between physical organoids and clinical practice, enabling high-throughput in silico experiments and active experiment design without added experimental burden and accelerating precise therapy, mechanism discovery and regulatory translation. • AIVOs are organoid-scale digital twins addressing physical limits like batch variability, data sparsity, and scalability. • A "Data-Model-Interaction" architecture integrates multimodal omics with hybrid models to reconstruct tissue dynamics. • Standardization and ethical frameworks are proposed to ensure interpretability and privacy for clinical translation.