Biohybrid Neural Interfaces: Integrating Living Cells with AI Systems for Adaptive Biomedical Devices

Abstract

The convergence of biological systems and artificial intelligence has given rise to biohybrid neural interfaces, a transformative paradigm in biomedical engineering aimed at creating adaptive, self-regulating medical devices. This study presents a comprehensive framework for integrating living neural cells with AI-driven computational architectures to develop responsive biomedical systems capable of real  time adaptation. Unlike conventional neural interfaces, which rely on static signal processing, the proposed biohybrid model leverages the inherent plasticity of biological neurons combined with machine learning algorithms to achieve dynamic feedback and control. The research explores the design, implementation, and evaluation of hybrid systems incorporating cultured neuronal networks, microelectrode arrays, and deep learning models. Experimental simulations demonstrate enhanced signal fidelity, improved adaptability, and reduced latency in response to physiological changes. The framework further incorporates reinforcement learning mechanisms to enable continuous system optimization based on biological feedback. Ethical considerations, biocompatibility challenges, and long-term stability are critically examined. The findings indicate that biohybrid neural interfaces hold significant potential for applications in neuroprosthetics, brain- computer interfaces, and personalized medicine. This work contributes to the emerging interdisciplinary domain at the intersection of neuroscience, artificial intelligence, and bioengineering, offering a scalable pathway toward next-generation intelligent biomedical devices capable of seamless integration with human physiology.