Artificial intelligence in nutrition and ageing research – A primer on the benefits

Abstract

Artificial intelligence (AI) is increasingly impacting multiple domains. The application of AI in nutrition and ageing research has significant potential to transform healthcare outcomes for the ageing population. This review provides critical insights into how AI techniques—such as machine learning, natural language processing, and deep learning—are used in the context of care for older people to predict health outcomes, identify risk factors, and enhance dietary assessments. Trained on large datasets, AI models have demonstrated high accuracy in diagnosing malnutrition, predicting bone mineral density abnormalities, and forecasting risks of chronic diseases, thereby addressing significant gaps in early detection and intervention strategies.

In addition, we review novel applications of AI in automating dietary intake assessments through image recognition and analysing eating behaviours; these offer innovative tools for personalised nutrition interventions. The review also discusses and showcases the integration of AI in research logistics, such as AI-assisted literature screening and data synthesis, which can accelerate scientific discovery in this domain.

Despite these promising advancements, there are critical challenges hindering the widespread adoption of AI, including issues around data quality, ethical considerations, and the interpretability of AI models. By addressing these barriers, the review underscores the necessity for interdisciplinary collaboration to best harness AI's potential.

Our goal is for this review to serve as a guide for researchers and practitioners aiming to understand and leverage AI technologies in nutrition and healthy ageing. By bridging the gap between AI's promise and its practical applications, this review directs future innovations that could positively affect the health and well-being of the ageing population.