{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:12:25Z","timestamp":1787163145379,"version":"build-2736575974"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pain Res."],"abstract":"<jats:p>Chronic pain that persists despite conventional therapy remains a major clinical and economic burden. Spinal cord stimulation (SCS) offers an alternative for treatment of refractory pain, yet about 30% of patients experience limited or no benefit due to the subjective nature of current patient selection processes. In this mini review, we explore how artificial intelligence (AI) and machine learning (ML) can optimize patient selection for and improve outcomes of SCS. Existing approaches to patient selection rely on patient-reported outcomes and physician judgment, which fail to capture multifactorial influences like clinical, psychological, and socioeconomic factors that determine success of pain treatment. AI and ML methods, including supervised learning, feature selection, and risk stratification, can analyze complex datasets from electronic health records to identify predictive variables associated with sustained pain relief. Integrating these technologies into the patient selection process may enhance precision in patient screening, reduce SCS device failure and explantation rates, and lower healthcare costs. Furthermore, adaptive learning algorithms can refine predictive models in real time, improving accuracy as new data emerge. With transition from subjective assessment to data-driven decision-making, AI-guided strategies have the potential to make SCS a more reliable, equitable, and cost-effective therapy for chronic pain management.<\/jats:p>","DOI":"10.3389\/fpain.2026.1827351","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:11:26Z","timestamp":1780035086000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging artificial intelligence to optimize neuromodulation in the treatment of patients with chronic pain"],"prefix":"10.3389","volume":"7","author":[{"given":"Rohail","family":"Siddiqi","sequence":"first","affiliation":[{"name":"Paul L. Foster School of Medicine, Texas Tech Health Science Center","place":["El Paso, TX, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Cheema","sequence":"additional","affiliation":[{"name":"Paul L. 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