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, Julia L. Finkelstein
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. Novel technologies including AI are becoming widely available and transforming healthcare and precision medicine. In this Perspective, the authors discuss how some of these technologies and innovations can help advance maternal and child health by complementing existing methods and enabling nutritional assessment on a larger scale and at an affordable cost in low-resource settings.