ArticleBMJ open2026
Voice as a predictive signal: protocol for a scoping review of AI in early childhood development.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEarly 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.