{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T13:49:16Z","timestamp":1792331356730,"version":"4.4.0"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T00:00:00Z","timestamp":1792281600000},"content-version":"vor","delay-in-days":290,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pain Research and Management"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This scoping review maps current AI applications, identifies implementation gaps, and demonstrates the need for systematic technology integration in pain medicine.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2019Malley framework, provided an integrative perspective on these rapidly evolving applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u2010time monitoring through connected devices and closed\u2010loop neuromodulation. Notable applications include AI\u2010guided spinal cord stimulation and wearable\u2010integrated pain profiling. Despite demonstrable clinical benefits across these domains, integration remains fragmented due to data governance challenges and educational gaps among practitioners.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/prm\/5521331","type":"journal-article","created":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T13:26:27Z","timestamp":1792329987000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence and Connected Technologies in Chronic Pain Management: A Scoping Review"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6124-1515","authenticated-orcid":false,"given":"Paul","family":"Elhomsy","sequence":"first","affiliation":[{"name":"Pain Management Department L\u2019Hopital Priv\u00e9 de L\u2019Aube  Troyes 10000 France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1080-8301","authenticated-orcid":false,"given":"Benjamin","family":"Reliquet","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery Dijon University Hospital  Dijon 21000 France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2945-7210","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Sudria","sequence":"additional","affiliation":[{"name":"Department of Maxillofacial Surgery Dijon University Hospital  Dijon 21000 France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,18]]},"reference":[{"key":"e_1_2_13_1_2","first-page":"E211","article-title":"The Analysis of Pain Research Through the Lens of Artificial Intelligence and Machine Learning","volume":"25","author":"Nagireddi J. 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