{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T10:25:36Z","timestamp":1787221536617,"version":"build-2736575974"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005322","name":"Recep Tayyip Erdogan \u00dcniversitesi","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early identification of neurodevelopmental conditions during the pre-school years (ages 3\u20136) is critical for timely intervention, yet traditional questionnaire-based screeners suffer from low positive predictive value, late detection, and inequitable access. Artificial intelligence (AI) and machine-learning (ML) methods promise more objective, scalable identification, and two devices (Cognoa Canvas Dx, 2021; EarliPoint Evaluation, 2023) are already US Food and Drug Administration (FDA)-authorized. Recent reviews summarize the technical literature, but few address how deployed tools perform on clinical validity, equity, and real-world implementation, particularly outside high-income, English-speaking settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following Joanna Briggs Institute methodology and the PRISMA extension for scoping reviews (PRISMA-ScR), we searched seven databases (1 January 2020\u201315 June 2026) and charted primary validation studies of AI\/ML developmental-screening tools for children aged 72 months or younger. We mapped tools by modality, diagnostic performance, regulatory status, geographic origin, and, as a structured analytic axis, demographic subgroup (equity) reporting and deployment readiness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Sixteen primary AI-tool studies met inclusion, spanning eight modality classes (eye-tracking, multimodal questionnaire-plus-video, computer-vision home\/clinical video, tablet\/smartphone digital phenotyping, voice\/acoustic ML, electronic-health-record risk prediction, questionnaire-based ML, and wearable sensors) and eight countries (USA, South Korea, Australia, Switzerland, France, Israel, China, Vietnam). Reported sensitivities ranged from approximately 75 to 99.2% and specificities from 78.9 to 95.5%. Only one tool demonstrated stable performance across sex, race, and ethnicity; subgroup-stratified validation was otherwise near-absent. No CE-marked autism-specific screening device was identified.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The evidence base has broadened in modality and geography but remains autism-concentrated and equity-blind, with a wide gap between analytic accuracy and primary-care deployment. We propose equity validation, deployment-readiness reporting, and an implementation pathway for low-resource guidance-center settings as priorities.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1913332","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T09:53:10Z","timestamp":1787219590000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial-intelligence\u2013based developmental screening for pre-school children (ages 3\u20136): a scoping review of deployed tools, clinical validity, equity, and implementation in guidance-center settings"],"prefix":"10.3389","volume":"17","author":[{"given":"Adem","family":"Arslan","sequence":"first","affiliation":[{"name":"Department of Child Care and Youth Services, Vocational School of Health Services, G\u00fcm\u00fc\u015fhane University","place":["G\u00fcm\u00fc\u015fhane, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatih","family":"G\u00fcl","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Internet of Things Research Laboratory, Department of Electrical-Electronics Engineering, Recep Tayyip Erdo\u011fan University","place":["Rize, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s13041-025-01269-9","article-title":"Early autism detection: a1 review of emerging technologies, biomarkers, and explainable ai approaches","volume":"19","author":"Agrawal","year":"2025","journal-title":"Mol. 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