{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T07:19:24Z","timestamp":1787555964612,"version":"build-2736575974"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Although Artificial Intelligence (AI) has shown considerable potential in the field of neurorehabilitation, systematic bibliometric evaluations of this interdisciplinary domain remain notably insufficient. Therefore, this review aims to conduct a quantitative synthesis of the literature related to AI in neurorehabilitation in order to clarify the developmental trajectory of the field, identify major research hotspots, and elucidate the underlying dynamics of knowledge evolution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following the PRISMA guidelines, relevant publications were retrieved from the Web of Science Core Collection between 2015 and 2026. A comprehensive bibliometric analysis was conducted through the integration of R Bibliometrix, VOSviewer, and CiteSpace. To complement the macro-level overview with a clinical perspective, clinical trial data from the PubMed database were additionally included to assess the current state of translational research in this field.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 487 publications were analyzed in this review. Since 2021, the field has shown steady annual growth. Among all countries, China produced the largest number of publications, whereas India demonstrated the greatest academic influence. The journal with the highest number of publications was Sensors, while Frontiers in Neuroscience had the highest average citations per article. Publishing trends and evolving themes indicate that AI in Neurorehabilitation is placing increasing emphasis on patient-centered, precision rehabilitation. The field has further evolved into three major research themes: AI-Driven Computational Intelligence, AI-Assisted Motor Recovery and Functional Rehabilitation, and Intelligent Rehabilitation Assessment and Personalized Clinical Decision-Making. Among the burst keywords, Data Models emerged as a future research hotspot. AI in neurorehabilitation may increasingly shift toward the field of personalized rehabilitation. In the future, the focus may no longer be limited to improving the performance of individual algorithms, but rather move toward a more human-centered, personalized, and intelligent approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This review provides a comprehensive quantitative synthesis of the AI in Neurorehabilitation field. The findings offer evidence-based references for clinicians and medical researchers, contributing to a comprehensive understanding of the developmental progress, core themes, and emerging trends in this field.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s44163-026-02038-w","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T06:22:02Z","timestamp":1787552522000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in neurorehabilitation research trends knowledge evolution and future directions from 2015 to 2026"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8437-7486","authenticated-orcid":false,"given":"Xi","family":"Ling","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1794-593X","authenticated-orcid":false,"given":"Hang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"issue":"1","key":"2038_CR1","doi-asserted-by":"publisher","first-page":"011001","DOI":"10.1088\/1741-2552\/aaf12e","volume":"16","author":"R Abiri","year":"2019","unstructured":"Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. 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