{
  "abstract": "Background: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition, and early identification is essential for timely intervention. Conventional diagnosis relies on clinical interviews, behavioral observations, caregiver reports, and standardized tools, which are time-consuming and resource-dependent. Artificial intelligence (AI) offers opportunities for screening and adjunctive diagnosis, but studies vary in data sources, model types, and validation methods, requiring systematic evaluation.\n\nMethods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and IEEE Xplore for studies of AI-based ASD detection. Eligible studies were required to provide ASD-versus-non-ASD diagnostic-accuracy data that could be extracted or reliably reconstructed at the independent participant or case level. Risk of bias and applicability were assessed using a modified QUADAS-AI tool. Pooled sensitivity and specificity were estimated with a bivariate Reitsma random-effects model. HSROC AUCs were calculated with bias-corrected and accelerated (BCa) 95% confidence intervals based on 5,000 study-level bootstrap replicates and were omitted when SROC fits were unstable. Sensitivity analyses addressed dataset overlap, model selection, and reference-standard quality.\n\nResults: Of 17,387 records, 26 studies were included. Pooled sensitivity was 0.908 (95% CI: 0.861–0.940), pooled specificity was 0.868 (95% CI: 0.830–0.898), and the HSROC AUC was 0.936 (BCa 95% CI: 0.860–0.973). A dataset-independent analysis of 21 studies yielded lower estimates (sensitivity 0.870, specificity 0.841, AUC 0.900). Leave-one-out results were generally stable. Funnel-plot asymmetry was not significant in the primary analysis and varied in direction and statistical significance across sensitivity analyses.\n\nConclusions: AI-based models showed relatively high pooled diagnostic performance for ASD detection, but substantial heterogeneity, methodological and applicability concerns, limited external validation, and potential non-independence of underlying datasets limit the certainty and generalizability of the evidence. Funnel-plot analyses did not provide consistent evidence of conventional small-study effects or selective reporting. AI should therefore be considered an adjunct rather than a standalone diagnostic tool. Prospective multicenter validation in representative populations, participant-level evaluation, clearly defined reference diagnoses, prespecified models and operating thresholds, and independent evaluation datasets are needed before broader clinical implementation.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Pediatrics, West China Second University Hospital, Sichuan University",
    "Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education"
  ],
  "article_type": "systematic review",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Qian Wang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Rong Luo"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Qianhui Wen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b7ykebt21myar.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1923851",
  "fulltext_url": "https://europepmc.org/articles/PMC13710828",
  "id": "b7ykebt21myar",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1923851/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Systematic Review"
    ],
    [
      "openalex",
      "2805|Autism Spectrum Disorder Research"
    ]
  ],
  "openalex": "W7221698081",
  "pdf_url": "https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1923851/pdf",
  "pmcid": "PMC13710828",
  "pmid": "42948225",
  "published": "2026-10-12",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/b7ykebt21myar.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:22Z",
      "native_id": "10.3389/fpubh.2026.1923851",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1923851/full"
    },
    {
      "first_seen": "2026-10-31T00:20:34Z",
      "native_id": "MED:42948225",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42948225"
    },
    {
      "first_seen": "2026-10-31T12:21:43Z",
      "native_id": "PMC13710828",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fpubh.2026.1923851"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2805|Autism Spectrum Disorder Research",
      "subfield": "neuroscience"
    }
  ],
  "title": "Artificial intelligence for detecting autism spectrum disorder: a systematic review and meta-analysis",
  "title_text": "Artificial intelligence for detecting autism spectrum disorder: a systematic review and meta-analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:43Z",
  "url": "https://research.draconitic.com/paper/b7ykebt21myar",
  "venue": "Frontiers in Public Health",
  "versions": []
}