Grootswagers Pol, Grootswagers Tijl
Wageningen University, Division of Human Nutrition and Health, 6708 PB Wageningen, the Netherlands.
The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, NSW, Australia; School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia.
Maturitas. 2025 Jul 7;200:108662. doi: 10.1016/j.maturitas.2025.108662.
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.
人工智能（AI）正日益影响多个领域。人工智能在营养与衰老研究中的应用具有巨大潜力，能够改变老年人群的医疗保健结果。本综述深入探讨了机器学习、自然语言处理和深度学习等人工智能技术是如何在老年人护理中用于预测健康结果、识别风险因素以及加强饮食评估的。基于大型数据集进行训练的人工智能模型在诊断营养不良、预测骨密度异常以及预测慢性病风险方面已显示出高准确率，从而填补了早期检测和干预策略方面的重大空白。此外，我们还综述了人工智能在通过图像识别和分析饮食行为实现饮食摄入量评估自动化方面的新应用；这些应用为个性化营养干预提供了创新工具。本综述还讨论并展示了人工智能在研究后勤方面的整合，例如人工智能辅助文献筛选和数据合成，这可以加速该领域的科学发现。尽管取得了这些令人鼓舞的进展，但仍存在一些关键挑战阻碍着人工智能的广泛应用，包括数据质量、伦理考量以及人工智能模型的可解释性等问题。通过解决这些障碍，本综述强调了跨学科合作以充分发挥人工智能潜力的必要性。我们的目标是让本综述成为研究人员和从业者的指南，帮助他们理解并利用人工智能技术促进营养与健康老龄化。通过弥合人工智能的前景与其实际应用之间的差距，本综述为未来的创新指明方向，这些创新可能对老年人群的健康和福祉产生积极影响。
