The Application of Artificial Intelligence to Enhance Spinal Cord Stimulation Efficacy for Chronic Pain Management: Current Evidence and Future Directions.

Abstract

Purpose of review: Chronic pain significantly impacts quality of life for millions globally, with spinal cord stimulation (SCS) as an established treatment for refractory chronic pain. However, traditional SCS therapies face limitations including inconsistent patient outcomes, challenges in patient selection, and difficulties in sustaining therapeutic efficacy. This review examines how artificial intelligence (AI) can enhance the efficacy and personalization of SCS therapy by optimizing patient selection, refining stimulation parameters, and enabling real-time adaptive adjustments.

Recent findings: Recent advances demonstrate that integrating AI with SCS significantly improves patient outcomes through predictive modeling for patient selection and real-time adaptive stimulation. Predictive analytics utilizing machine learning algorithms have successfully identified patient cohorts most likely to benefit from SCS therapy, enhancing response rates and reducing suboptimal outcomes. Closed-loop AI systems incorporating physiological feedback, such as evoked compound action potentials (ECAPs), dynamically optimize stimulation parameters, resulting in sustained pain relief, decreased programming burden, and improved device longevity. Despite these promising results, critical challenges persist, particularly related to data standardization, ethical considerations, and regulatory compliance. AI holds transformative potential for spinal cord stimulation, offering increased precision, personalization, and therapeutic efficiency in managing chronic pain. Although early results are encouraging, comprehensive clinical validation and multidisciplinary collaboration remain essential. Addressing ethical, regulatory, and data management challenges will be critical for widespread adoption of AI-enhanced SCS therapies in routine clinical practice.