ReviewEuropean respiratory review : an official journal of the European Respiratory Society2026
Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.
Review in European respiratory review : an official journal of the European Respiratory Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has advanced rapidly in adult sleep medicine, but its role in children under 2 years old remains unclear. This scoping review maps the current evidence on AI-enabled diagnostics in neonates, infants and toddlers, identifying challenges, opportunities and research priorities.
methodsA structured scoping review was undertaken up to April 2025, incorporating peer-reviewed studies, normative datasets, regulatory reports and evidence on consumer and commercial technologies. Studies focusing on children under 2 years were prioritised, with older cohorts reviewed where relevant to translation.
findingsNo AI tool is validated for independent diagnostic use in this age group. Research prototypes, including automated polysomnography scoring, convolutional neural networks for oximetry and multimodal wearable or radar-based systems, show feasibility but remain experimental. Commercial platforms such as SleepImage, Belun Sleep Health and WatchPAT exclude children under 2 years, while consumer monitors including Owlet, Nanit and Miku are widely used but lack clinical validation.
conclusionAI in sleep diagnostics for children under 2 years remains an emerging field with high potential but no current clinical readiness. Progress will depend on the development of infant-specific datasets, improved artefact handling, simplified monitoring strategies and human-in-the-loop workflows. Longer-term opportunities include prognostic modelling, circadian analysis and predictive closed-loop systems, but equity and regulatory clarity must guide translation to ensure safe and globally relevant impact.
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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.