Advances in artificial intelligence and precision nutrition approaches to improve maternal and child health in low resource settings

Saurabh Mehta (), Samantha L. Huey, Shah Mohammad Fahim, Srishti Sinha, Kripa Rajagopalan, Tahmeed Ahmed, Rob Knight and Julia L. Finkelstein ()
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Saurabh Mehta: Cornell University
Samantha L. Huey: Cornell University
Shah Mohammad Fahim: Cornell University
Srishti Sinha: Cornell University
Kripa Rajagopalan: Cornell University
Tahmeed Ahmed: International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR, B)
Rob Knight: University of California San Diego
Julia L. Finkelstein: Cornell University

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract Malnutrition continues to be a major threat to health, particularly maternal and child health in low resource settings, resulting in impairments in cognitive function, growth, and development, and metabolic diseases later in life. Nutritional assessment is a cornerstone of any successful nutrition intervention or program whether in the community or at the clinic. Improved computational power and advances in technology may enable precision nutrition-based approaches for maternal and child health, which can complement current methods for nutritional assessment to identify clinical, biochemical, microbiome-related, social, and environmental characteristics to predict responses to nutritional interventions or programs. Precision nutrition has the potential to complement program monitoring, efficacy evaluation, and ultimately to inform design of interventions to improve maternal and child health.

Date: 2025
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DOI: 10.1038/s41467-025-62985-3

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