{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T01:49:12Z","timestamp":1789782552204,"version":"4.0.1"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"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. Public Health"],"abstract":"<jats:p>Autism spectrum disorder (ASD) is a common, lifelong neurodevelopmental condition whose recorded prevalence, diagnostic delays, and uneven distribution of specialist services create a growing public health challenge. Conventional screening and diagnostic pathways depend heavily on scarce specialist expertise, contributing to long waiting times and unequal access across income settings, regions, sex, ethnicity, language, and social position. This narrative review synthesises current applications of artificial intelligence (AI) and machine learning in autism screening, diagnostic support, intervention, and longitudinal monitoring, and reframes the evidence through a public health and health equity lens. We argue that AI\u2019s most important contribution to autism care is unlikely to lie in marginal improvements in classification accuracy alone. Rather, its potential value lies in expanding access, supporting task-sharing, shortening diagnostic pathways, enabling population-oriented screening, and reaching under-recognised groups such as girls and women, adults, ethnic and linguistic minorities, and populations in low-resource settings. At the same time, AI may create an equity paradox: technologies intended to reduce disparities may reproduce or amplify them if they are trained on non-representative data, deployed across a digital divide, or governed without adequate attention to privacy, accountability, and community trust. Whether AI narrows or widens autism-related health inequalities will depend on choices about data diversity, low-resource design, co-design with autistic communities, equity-sensitive evaluation, clinical integration, and proportionate regulation.<\/jats:p>","DOI":"10.3389\/fpubh.2026.1898818","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:34:07Z","timestamp":1783920847000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial intelligence, autism care, and health equity: a public health narrative review"],"prefix":"10.3389","volume":"14","author":[{"given":"Xugao","family":"Han","sequence":"first","affiliation":[{"name":"School of Medical Humanities, Nanjing University of Chinese Medicine","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyan","family":"Weng","sequence":"additional","affiliation":[{"name":"School of Tourism Management, Nanjing Institute of Tourism and Hospitality","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houxi","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S2215-0366(24)00363-8","article-title":"The global epidemiology and health burden of the autism spectrum: findings from the global burden of disease study 2021","volume":"12","year":"2025","journal-title":"Lancet Psychiatry"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s13052-022-01310-w","article-title":"The global prevalence of autism spectrum disorder: a comprehensive 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