Bai, L., Wang, J., Lai, Y., & Su, J. (2025).
EngMedicine, 2(4), 100106.
Abstract
The convergence of brain organoids and artificial intelligence (AI) has driven the development of organoid intelligence (OI), a new paradigm for constructing human-level cognitive models. Brain organoids derived from human stem cells exhibit self-organizing neural networks with dynamic activity and plasticity, offering a biologically based alternative to conventional AI systems. The integration of living networks with computational frameworks enables the design of closed-loop systems that combine the adaptability of biological tissues with the scalability and interpretability of AI. This approach not only provides a novel model for studying human cognition but also opens new pathways for biologically inspired computing. The development of such hybrid systems requires interdisciplinary collaboration among stem cell biology, bioengineering, neuroscience, and machine learning. The long-term goal is to establish biohybrid platforms capable of learning, memory formation, and task-specific computation, thereby redefining our understanding of intelligence and enabling the next generation of neurotechnologies.
Highlights
• Organoid Intelligence (OI) combines brain organoids and AI.
• OI creates biologically embodied models for human-level cognition.
• Biohybrid platforms can learn, remember, and perform computations.
• OI requires interdisciplinary collaboration for development.
Here are some general thoughts:
We are witnessing the infancy of true synthetic biological intelligence. While current applications are constrained to pattern recognition and disease modeling, the long-term trajectory completely disrupts the binary view of technology as "artificial" and biology as "natural." It forces tech developers and ethicists alike to confront a reality where the next generation of advanced intelligence might not be coded, but grown.
