Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation

Neurodegenerative diseases (NDs) are progressive and incurable central nervous system disorders characterized by the accumulation of pathological proteins and the loss of neurons. Although stem cell transplantation offers a new treatment option, its clinical application is severely hindered due to imprecise delivery, low survival rate, and undirected differentiation. Many studies have used nanomaterials to enhance stem cell therapy. However, the rational design of these multifunctional nanomaterials often requires a large number of experiments and calculations to determine the optimal parameters. Meanwhile, the diagnosis of NDs and the design of nanomaterials are being profoundly influenced by artificial intelligence (AI) and data-driven modeling. Based on these advancements, we propose that AI can guide personalized nano-enhanced stem cell therapies. This review explores how machine learning (ML) and deep learning (DL) can address the current challenges in stem cell therapy and nano-enhanced stem cell therapies. More importantly, it provides a systematic framework for integrating AI across the entire nano-enhanced stem cell therapy. We analyzed how AI can optimize the design of nanobiological materials, thereby enhancing the survival rate of stem cells, targeted delivery, directing differentiation, and controlling the release of loaded drugs. Additionally, we proposed that AI can be used for post-transplant tracking and prognosis management. Beyond summarizing parallel advancements, this review proposes a closed-loop system that integrates patient-specific data, AI-driven design, and real-time monitoring, aiming to advance truly personalized medicine for NDs.