Artificial Intelligence Virtual Organoid Laboratories (AIVO-Labs)

Abstract

Organoids reproduce important tissue features, but variability in biomaterials and culture, labor-intensive workflows, limited standardization, and predominantly endpoint readouts constrain reproducibility and longitudinal interpretation. We propose Artificial Intelligence Virtual Organoid Laboratories (AIVO-Labs), a closed-loop experimental paradigm that couples physical organoid experiments with Artificial Intelligence Virtual Organoids (AIVOs) as continuously updated digital twins. Unlike an AIVO, which represents an individual organoid-scale computational counterpart, an AIVO-Lab is a laboratory-level, cross-experimental platform that coordinates data acquisition, model calibration, experiment selection, and prospective wet-lab validation across repeated cycles. In this bidirectional workflow, each physical result updates the AIVO, while model predictions prioritize the next experiment, directly addressing slow iteration, variable protocols, and endpoint-focused analysis. The framework builds on Artificial Intelligence Virtual Cells (AIVCs), which encode multimodal cell states as universal representations that can be interrogated by virtual instruments, and extends them to organoid scale through models of cell-cell and cell-matrix interactions. We outline a three-layer architecture comprising data, model, and interaction layers and discuss applications in drug screening, mechanism and resistance mapping, developmental and disease trajectories, and toxicology. AIVO-Labs are presented as a research framework whose predictions require prospective validation, transparent uncertainty estimates, and appropriate governance.

Keywords

Artificial intelligence / Organoid / AIVOs / AIVCs / AIVO-Labs

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