Artificial intelligence in sleep diagnostics for children under 2 years of age: state of the evidence and future directions.

BACKGROUND: Artificial 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. METHODS: A 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. FINDINGS: No 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. CONCLUSION: AI 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.