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Kong, J., Lee, H., Kim, D., Han, S.K., Ha, D., Shin, K., et al. (2020) Network-Based Machine Learning in Colorectal and Bladder Organoid Models Predicts Anti-Cancer Drug Efficacy in Patients. Nature Communications, 11, Article No. 5485.
https://doi.org/10.1038/s41467-020-19313-8

has been cited by the following article:

• TITLE: Organoid Intelligence: Bridging the Gap between Biological and Artificial Neural Networks

  AUTHORS: Gobind Naidu, Harwindar Singh, Vicknesh Krishnan

  KEYWORDS: Organoid Intelligence, Biocomputing, Neural Network, Ethical Implications, Neurological Disease Modeling

  JOURNAL NAME: International Journal of Intelligence Science, Vol.16 No.1, December 22, 2025

  ABSTRACT: Organoid Intelligence (OI) represents a groundbreaking convergence of biology and technology, aiming to redefine biocomputing using brain organoids—three-dimensional neural structures derived from human stem cells. This journal explores OI’s core concepts, its ability to process information compared to silicon-based artificial intelligence (AI), and its promising applications in healthcare, such as modelling neurological diseases, advancing personalized medicine, and enhancing AI-supported drug discovery. While OI offers exciting possibilities, we temper enthusiasm with a realistic look at its current limits, presenting new data from a small-scale experiment on organoid electrical activity. We also deepen the discussion of ethical challenges, including moral status and consent, and propose initial steps toward addressing technical hurdles like scalability. OI’s potential is vast, but its development demands careful ethical and practical navigation.