AI-Driven Flexible and Soft Bioelectronics

Recent advancements in flexible and soft bioelectronics have enabled continuous, long-term stable interrogation and intervention of biological systems. However, effectively utilizing the interrogated data to modulate biological systems to achieve specific biomedical and biological goals remains a challenge. In this study, we introduce an AI-driven bioelectronics system that integrates tissue-like, soft and flexible bioelectronics with cyber learning algorithms to create a long-term, real-time adaptive, bidirectional bioelectronic interface. When integrated with biological systems as an AI-cyborg system, this framework continuously adapts and optimizes stimulation parameters based on stable cell state mapping, allowing for real-time, closed-loop feedback through tissue-embedded flexible electrode arrays. Applied to human pluripotent stem cell-derived organoids, this AI-cyborg system identifies optimal stimulation conditions that accelerate functional maturation. The effectiveness of this approach is validated through enhanced action potential, increased signal propagation, and improved molecular phenotyping states.