Artificial Intelligence and Connected Technologies in Chronic Pain Management: A Scoping Review

Abstract

Background Chronic pain affects over 1.5 billion people worldwide, facing persistent challenges in assessment subjectivity, treatment variability, and scalability (i.e., the limited ability to deliver individualized care to large populations within current clinical systems). Artificial intelligence (AI) and connected health technologies have transitioned from experimental tools to clinical reality in pain management. Objective This scoping review maps current AI applications, identifies implementation gaps, and demonstrates the need for systematic technology integration in pain medicine. Methods We conducted a targeted literature search across PubMed, Scopus, MEDLINE, Web of Science, and Google Scholar. From 487 identified articles, 23 met inclusion criteria for a detailed analysis. A scoping review approach, following the Arksey and O’Malley framework, provided an integrative perspective on these rapidly evolving applications. Results AI implementations span four domains: (1) objective pain assessment via multimodal analysis (facial recognition, neurophysiology, and imaging); (2) personalized treatment strategies using machine learning to optimize therapeutic responses; (3) predictive analytics for risk stratification (postoperative pain and opioid misuse); and (4) real‐time monitoring through connected devices and closed‐loop neuromodulation. Notable applications include AI‐guided spinal cord stimulation and wearable‐integrated pain profiling. Despite demonstrable clinical benefits across these domains, integration remains fragmented due to data governance challenges and educational gaps among practitioners. Conclusions AI is demonstrably improving chronic pain outcomes, yet integration remains fragmented due to data governance issues and educational gaps. Professional societies must establish specialized AI committees, clinical guidelines, and structured training. As this transformation occurs, pain specialists must lead this evolution rather than adapt reactively to external changes.