{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T17:33:04Z","timestamp":1793727184361,"version":"4.6.1"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["WORK: A Journal of Prevention, Assessment &amp; Rehabilitation"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The rapid integration of Artificial Intelligence (AI) into healthcare and industry is often framed as a threat to human roles. Within rehabilitation and occupational health, this polarized discourse risks obscuring more nuanced possibilities for human-AI collaboration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This article challenges the dominant automation narrative by arguing that AI is instead catalyzing a symbiotic shift within rehabilitation and return-to-work contexts. It aims to conceptualize how AI can augment human expertise to create more personalized, efficient, and equitable outcomes across the rehabilitation-to-work continuum.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A structured literature exploration was conducted across PubMed\/MEDLINE, IEEE Xplore, ACM Digital Library, PsycINFO, and Scopus. An initial search covered literature published between 2018 and March 2024, followed by an updated search conducted on 3 April 2026, which identified five additional relevant publications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The analysis reveals three interconnected domains of AI application. First, in clinical rehabilitation, AI enables data-driven precision through personalized therapeutic programs, computer vision for objective movement analysis, and predictive analytics for prognostic forecasting. Second, AI facilitates safer and more sustainable return-to-work transitions through objective functional capacity evaluations and AI-assisted job coaching, as illustrated in a hypothetical scenario. Third, AI fosters inclusive workplace integration through intelligent accommodations and skills redeployment, supporting sustained employment for individuals with disabilities or chronic conditions. The review also identifies critical ethical imperatives, including data privacy, algorithmic bias, and equitable access, that must guide this integration to prevent the exacerbation of existing disparities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>When implemented ethically and collaboratively, AI does not replace human judgment but liberates rehabilitation professionals and workplace managers to focus on higher-order tasks of empathy, strategy, and complex decision-making. The future of work, therefore, lies not in automation alone, but in a human-AI partnership that builds a more adaptive, inclusive, and ultimately more humane environment. In this emerging paradigm, individuals are not simply returned to work but are empowered to thrive.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/10519815261491691","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:52:21Z","timestamp":1790610741000},"page":"917-926","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["The symbiotic shift: How AI is reshaping rehabilitation and the future of work"],"prefix":"10.1177","volume":"85","author":[{"given":"Hasham Khaleel","family":"Al Musawi","sequence":"first","affiliation":[{"name":"Library, Acknowledge Education"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10926-020-09910-1"},{"key":"e_1_3_3_3_2","first-page":"15","article-title":"Strategies for supporting disability-inclusive employment in the future of work","volume":"35","author":"Hossain S","year":"2025","unstructured":"Hossain S, Nasir K, Carolli A, et al. Strategies for supporting disability-inclusive employment in the future of work. J Occup Rehabil 2025; 35: 15\u201328.","journal-title":"J Occup Rehabil"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_3_3_5_2","article-title":"A roadmap for the use of AI in rehabilitation: scoping review","volume":"9","author":"L\u00f3pez DM","year":"2021","unstructured":"L\u00f3pez DM, Blobel B, Gonz\u00e1lez C. A roadmap for the use of AI in rehabilitation: scoping review. 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