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

Clinical Snapshot

Key Findings

• P Value: Not applicable for narrative review

• Effect Size: Not quantified - narrative synthesis only

• Primary Outcome: AI shows promise across neuropathic pain management domains but requires validation

• Nnt Or Sensitivity: 24-month sustained benefit reported for SCS in real-world cohorts

• Confidence Interval: Not provided

Clinical Application

Currently limited by single-centre studies and lack of prospective validation Relevant to Australian pain services, particularly for SCS programs under PBS item 39130. TGA approval would be required for AI-guided medical devices. Aligns with RACGP chronic pain management guidelines emphasising multimodal approaches Patients with heterogeneous neuropathic pain conditions requiring personalised treatment approaches

Abstract

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.

References

1. 1.Lo Bianco, G., Deer, T. R., Occhigrossi, F., Diwan, S., Mercieri, M., D'Angelo, F. P., Martinez, S. M., Day, M. R., Navani, A., & Abd-Elsayed, A. (2026). Artificial Intelligence in Neuropathic Pain: From Mechanisms to Neuromodulation and Regenerative Strategies. Current Pain and Headache Reports. https://doi.org/10.3390/biomedicines13030636