Artificial intelligence to support infant feeding and nutritional care: a scoping review

Infant feeding and nutrition are critical for growth, development, and long-term health outcomes. As paediatric nurses play a central role in infant monitoring and feeding practices, there is growing interest in innovative approaches to support early assessment and personalised nutritional care. Artificial intelligence (AI) is increasingly explored to support clinical decision-making; however, its applications in infant feeding and nutrition have not been comprehensively synthesised.

To map AI applications in infant feeding and nutrition and examine their potential implications for clinical care and paediatric nursing practice.

This scoping review followed Arksey and O'Malley's framework and adhered to the PRISMA-ScR guidelines. A comprehensive search of research databases was conducted across seven electronic databases, grey literature, and trial registers, including studies published from inception to 26 May 2026. Two reviewers independently screened titles and abstracts and extracted and synthesised data.

Twenty-nine studies were included. Seven broad AI application areas were identified: 1) prediction and classification of nutritional status, 2) estimation and monitoring of growth, 3) providing nutritional recommendations, 4) analysing nutritional practices and variations, 5) suckling and feeding behaviour analysis, 6) feeding method and behaviour monitoring, and 7) prediction and classification of feeding status.

AI demonstrates potential to support paediatric nurses and healthcare professionals by enabling more timely, personalised interventions to improve nutritional care and long-term infant health outcomes. However, current evidence remains largely developmental, highlighting the need for real-world clinical studies to determine the safety, effectiveness, and integration of AI technologies into paediatric care.

Open Science Framework (Registration Number: b45tv).