Artificial Intelligence in Spinal Cord Stimulation and Neuromodulation: A Narrative Review of Clinical Applications, Emerging Evidence, and Future Directions

Abstract

Spinal Cord Stimulation (SCS) is known as an established neuromodulatory therapy which is used for refractory chronic pain; however, its clinical outcomes remain heterogeneous due to limitations in patient selection, subjective outcome assessment, and trial-and-error programming strategies. Recent advances into Artificial Intelligence (AI) as well as Machine Learning (ML) have further introduced data-driven approaches for addressing such challenges through leveraging high-dimensional clinical, electrophysiological, imaging, and patient-reported datasets. The present narrative review article summarises emerging role of AI into SCS and neuromodulation, which is focused on AI-assisted selection of patients, intelligent programming, closed-loop adaptive systems, as well as clinical evidence. AI techniques which are inclusive of supervised and unsupervised learning, Deep Learning (DL), and Reinforcement Learning (RL), enable improved prediction of responders, phenotyping of chronic pain populations, and realtime optimisation of stimulation parameters. Early clinical and pilot studies suggest promising improvements into personalisation, therapeutic consistency; however, evidence still remains limited by small sample sizes, heterogeneity, also lack of large prospective trials. Ethical, regulatory, data-governance challenges like privacy, algorithmic bias, transparency, accountability further act as additional barriers for widespread adoption of AI into SCS as well as neuromodulation. Future research must focus on validation at multicenter-level, standardised-type of data frameworks, explainable AI along with adaptive regulatory pathways. AI-based SCS thus holds very significant potential into advancement of precision neuromodulation, provided responsible integration, rigorous clinical validation are adequately achieved. The current narrative review article uniquely integrates current and emerging applications of AI across full SCS pathway while also further critically highlighting evidence gaps, future directions for precision neuromodulation.