The Application of Artificial Intelligence in Neuromodulation

This review explores the transformative impact of artificial intelligence (AI) on neuromodulation, highlighting its shift from fixed protocols to personalized therapy. AI facilitates this through two main approaches: pre-treatment optimization, which uses multimodal data for pretreatment planning and outcome prediction; and dynamic adaptation, enabling real-time parameter adjustment in closed-loop systems like interactive neurostimulation and brain-computer interfaces. Furthermore, integration with telemedicine not only extends therapeutic reach but also accumulates real-world data, which is essential for scaling and refining AI models. However, clinical implementation faces challenges including data standardization, model interpretability, regulatory approval, and clinical workflow integration. Overcoming these challenges is essential to fully realize the potential of AI in creating more effective, adaptive, and patient-specific neuromodulation therapies.