Artificial Intelligence in Neuropathic Pain: From Mechanisms to Neuromodulation and Regenerative Strategies

Purpose of review: Neuropathic pain remains challenging due to its heterogeneous mechanisms and variable treatment response. This narrative review evaluates recent advances in artificial intelligence (AI) and machine learning (ML) for patient phenotyping, treatment selection, outcome prediction, neuromodulation, and regenerative therapies. Recent findings: ML-based phenotyping integrates genomic, neuroimaging, sensory, and behavioral data. Multimodal automatic pain assessment is approaching clinical deployment, supported by recent expert consensus. Large language models have been benchmarked against multidisciplinary teams for spinal cord stimulation (SCS) candidate selection. Real-world cohort data confirm sustained 24-month SCS benefit. AI is increasingly applied to regenerative interventions, including pulsed radiofrequency, which is now linked to epigenetic remodeling of dorsal root ganglion pathways. Most studies remain single-center and retrospective. AI may enable mechanism-based stratification, individualized neuromodulation programming, and rational integration of regenerative therapies in the treatment of neuropathic pain. Robust prospective validation, transparent reporting, and ethical safeguards are required before approaching translational maturity in selected settings.