{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T22:02:09Z","timestamp":1790373729062,"version":"4.1.0"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) technologies are increasingly integrated into health contexts and may facilitate health management and healthcare support. However, not all populations may benefit equally from advances in AI health applications, especially those with disadvantaged social status.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study examined sociodemographic disparities in AI use for health among adults in Mainland China and explored how social positions may intersect in shaping AI use for health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A nationally representative online sample of Chinese adults (N\u2009=\u20091200) completed questionnaire items assessing six forms of AI use for health and sociodemographic variables, including sex, age, income, education, and insurance coverage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Participants reported low to moderate overall AI use for health. Higher income was associated with greater use of AI-powered wearable health reports, AI-assisted hospital care, and AI-based tools for mental health, but lower use of AI-based medical consultation chatbots. Having commercial health insurance was consistently positively associated with various AI health use, whereas having rural cooperative insurance was not associated with AI use behaviors. In addition, age significantly interacted with both income and insurance status in predicting overall AI use for health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Findings suggest that AI health use in Mainland China varies across sociodemographic groups and may reflect broader inequalities in healthcare access and resources.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/tbm\/ibag068","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:48:21Z","timestamp":1789040901000},"source":"Crossref","is-referenced-by-count":0,"title":["Exploring sociodemographic disparities in AI use for health: findings from a nationally representative study in Mainland China"],"prefix":"10.1093","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1985-9064","authenticated-orcid":false,"given":"Tianen","family":"Chen","sequence":"first","affiliation":[{"name":"University of South Florida Department of Communication, , Tampa, FL,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mulin","family":"Jiang","sequence":"additional","affiliation":[{"name":"City University of Hong Kong Department of Media and Communication, , Hong Kong,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengbin","family":"Hu","sequence":"additional","affiliation":[{"name":"Fudan University Fudan Development Institute, , Shanghai,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6682-1712","authenticated-orcid":false,"given":"Fei","family":"Shen","sequence":"additional","affiliation":[{"name":"City University of Hong Kong Department of Media and Communication, , Hong Kong,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"2026092517471836600_ibag068-B1","doi-asserted-by":"publisher","first-page":"407","DOI":"10.59249\/NKOY5498","article-title":"Artificial intelligence to improve patient understanding of radiology reports","volume":"96","author":"Amin","year":"2023","journal-title":"Yale J Biol Med"},{"key":"2026092517471836600_ibag068-B2","doi-asserted-by":"publisher","first-page":"18994","DOI":"10.1038\/s41598-024-70073-7","article-title":"Innovation and challenges of artificial intelligence technology in personalized healthcare","volume":"14","author":"Li","year":"2024","journal-title":"Sci Rep"},{"key":"2026092517471836600_ibag068-B3","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1038\/s41746-018-0058-9","article-title":"Health intelligence: How artificial intelligence transforms 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