{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:17:00Z","timestamp":1783610220556,"version":"3.55.0"},"reference-count":103,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"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. Neurol."],"abstract":"<jats:p>Spinal cord injury (SCI) is a severe neurological condition often leading to significant disability, with causes including trauma, tumors, and spinal cord pathologies. Affected patients commonly present with limb motor dysfunction and persistent chronic pain. Current clinical management of SCI primarily relies on pharmacological and surgical interventions; however, neurological recovery remains limited due to the restricted neural regenerative capacity and the high costs associated with long-term rehabilitation, which together impose a substantial socioeconomic burden. AI monitoring, encompassing data acquisition and preprocessing, algorithmic modeling, real-time analysis, and decision support, offers the potential to continuously track vital signs, assess neurological function, and support pain management in patients with SCI through the integration of algorithms and real-time data analysis. Additionally, by facilitating the development of personalized rehabilitation plans, AI monitoring may enable more precise and dynamic postoperative management. Traditional post-SCI monitoring largely depends on subjective assessments and standardized interventions, which are often constrained by delayed responses and limited individual adaptation. As such, the integration of AI monitoring into the treatment process has attracted increasing attention from clinicians and researchers. This article aims to summarize the preliminary research progress and emerging applications of AI monitoring in postoperative pain management and functional rehabilitation for SCI, with the goal of providing a critical overview of its potential roles and current limitations in advancing post-SCI care.<\/jats:p>","DOI":"10.3389\/fneur.2026.1838329","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T14:49:39Z","timestamp":1783608579000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A narrative review of AI monitoring in postoperative pain management and functional rehabilitation for spinal cord injury"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiaoqing","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Spinal Trauma, Shaoxing City Keqiao District TCM Hospital Medical Alliance General Hospital","place":["Shaoxing, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Sheng","sequence":"additional","affiliation":[{"name":"Department of Spinal Trauma, Shaoxing City Keqiao District TCM Hospital Medical Alliance General Hospital","place":["Shaoxing, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"102598","DOI":"10.1016\/j.arr.2024.102598","article-title":"Global, regional, and national burden of spinal cord injury from 1990 to 2021 and projections for 2050: a systematic analysis for the global burden of disease 2021 study","volume":"103","author":"Lu","year":"2025","journal-title":"Ageing Res Rev"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1089\/neu.2020.7473","article-title":"Characterizing natural recovery after traumatic spinal cord 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