{
  "abstract": "Introduction Early childhood development (ECD) interventions support children aged 0–5 years, including those typically developing, at risk of delays or diagnosed with motor, cognitive, language or social-emotional disorders. Current assessments face barriers like limited access, high costs, intermittent evaluations and invasive methods. Voice offers a non-invasive digital biomarker, with artificial intelligence (AI) and machine learning (ML) enabling analysis of vocal features linked to developmental trajectories. This scoping review protocol synthesises evidence on AI-driven voice biomarkers for early detection, monitoring and management of ECD outcomes in healthy, at-risk or impaired children under five.Methods and analysis This scoping review protocol adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and the Arksey and O’Malley framework, enhanced by Joanna Briggs Institute recommendations. Eligibility criteria follow the Population, Concept and Context framework: population (children 0–5 years), concept (AI/ML voice biomarker analysis), context (early detection, monitoring, management of developmental outcomes). Comprehensive searches target PubMed/MEDLINE, Scopus, Web of Science, Embase, IEEE Xplore, CINAHL and grey literature sources for peer-reviewed English/Persian articles from January 2015 onwards. Two independent reviewers screen titles/abstracts/full texts and extract data on clinical applications, recording protocols, acoustic features, AI models, demographics and implementation factors. Discrepancies were resolved via discussion or a third reviewer. Results were presented narratively with tables, charts and figures addressing research questions on voice as a predictive signal.Ethics and dissemination The Research Ethics Committee of Tabriz University of Medical Sciences approved this protocol, confirming ethical compliance absent patient involvement. Findings were disseminated via peer-reviewed journals, conferences and institutional seminars to inform AI integration in paediatric health informatics for equitable child development support.Systematic review registration Not registered.",
  "authors": [
    {
      "affiliations": [
        "Student Research Committee (SRC), Tabriz University of Medical Sciences, Tabriz, East Azerbaijan Province, Iran (the Islamic Republic of)",
        "Pediatric Health Research Center, Tabriz University of Medical Sciences, Tabriz, East Azerbaijan Province, Iran (the Islamic Republic of)",
        "Iranian Center of Excellence in Health Management, Department of Health Service Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, East Azerbaijan Province, Iran (the Islamic Republic of)"
      ],
      "name": "Mehrdad Amir-Behghadami"
    },
    {
      "affiliations": [
        "Student Research Committee (SRC), Tabriz University of Medical Sciences, Tabriz, East Azerbaijan Province, Iran (the Islamic Republic of)",
        "Pediatric Health Research Center, Tabriz University of Medical Sciences, Tabriz, East Azerbaijan Province, Iran (the Islamic Republic of)"
      ],
      "name": "Seifollah Heidarabadi"
    }
  ],
  "title": "Voice as a predictive signal: protocol for a scoping review of AI in early childhood development",
  "uid": "61dba508-d5e6-5498-aa86-25b5639e83c0"
}