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Updated: Jan 16, 2026

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Artificial Intelligence Virtual Organoids (AIVOs).

Long Bai1,2,3,4, Jiacan Su1,2,5,3

  • 1Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.

Bioactive Materials
|January 15, 2026
PubMed
Summary
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Artificial Intelligence Virtual Organoids (AIVOs) offer scalable, digital twins of organoids, overcoming limitations of traditional methods. These silicon organoids enable high-throughput in silico experiments for drug discovery and clinical applications.

Area of Science:

  • Biotechnology and Computational Biology
  • In vitro modeling and digital twins

Background:

  • Organoid platforms have advanced in vitro human biology research.
  • Current organoid models face challenges like batch variability, limited longitudinal data, and scalability issues.

Purpose of the Study:

  • To introduce Artificial Intelligence Virtual Organoids (AIVOs) as a novel computational approach.
  • To define the architecture, construction, and evaluation strategies for AIVOs.

Main Methods:

  • AIVOs are organoid-scale digital twins using virtual cells and organoids as executable units.
  • They integrate multimodal, longitudinal data into universal state representations.
  • Hybrid mechanistic modules emulate biological dynamics and transport processes.
Keywords:
ApplicationArtificial intelligenceConstructionSilicon organoidsVirtual organoids

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Main Results:

  • AIVOs enable emulation of assays and perturbations using virtual instruments.
  • They facilitate applications in drug screening, disease subtyping, and clinical decision support.
  • The approach provides a transparent and reproducible bridge between physical organoids and clinical practice.

Conclusions:

  • AIVOs address limitations of physical organoids, offering scalable, high-throughput in silico experimentation.
  • Key challenges include data acquisition, computational scalability, interpretability, and governance.
  • AIVOs accelerate mechanism discovery, precise therapy development, and regulatory translation.