Abstract
Neural stem cells (NSCs) hold significant potential in neural regenerative medicine, yet research faces multiple challenges such as cellular heterogeneity, unclear microenvironment interactions, and low clinical translation efficiency. In recent years, the rapid development of artificial intelligence (AI) technologies has provided new ideas and tools to address these issues. This paper reviews the current applications of AI in fundamental NSCs research, including intelligent identification, deep learning-driven subtype analysis, spatial microenvironment deconstruction, and dynamic analysis of neural differentiation. Additionally, we discuss several key AI technologies not yet applied to NSCs research, such as generative adversarial networks, graph neural networks, and self-supervised learning, as well as their potential applications in cell classification, interaction network analysis, and morphological feature extraction. Although AI technologies show great promise in NSCs research, challenges remain regarding data quality, model robustness, and interpretability. Therefore, future research should focus on establishing high-quality standardized multimodal data platforms and integrating biological knowledge to enhance model interpretability, thereby deepening the understanding of NSCs biological characteristics and differentiation mechanisms and advancing personalized therapies.