Ferreira Danton Diego, Ferreira Lívia Garcia, Amorim Katiúcia Alves, Delfino Deyvis Cabrini Teixeira, Ferreira Ana Cláudia Barbosa Honório, Souza Leandra Passarelli Castro E
Department of Automatic, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil.
Nutrition and Health Graduate Program, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil.
Curr Nutr Rep. 2025 Mar 15;14(1):47. doi: 10.1007/s13668-025-00635-2.
To conduct an overview of the potentialities of artificial intelligence in precision nutrition.
A keyword co-occurrence analysis of 654 studies on artificial intelligence (AI) and precision nutrition (PN) highlighted the potential of AI techniques like Random Forest and Gradient Boosting in improving personalized dietary recommendations. These methods address gastrointestinal symptoms, weight management, and cardiometabolic markers, especially when incorporating data on gut microbiota. Despite its promise, challenges like data privacy, bias, and ethical concerns remain. AI must complement healthcare professionals, necessitating clear guidelines, robust governance, and ongoing research to ensure safe and effective applications. The integration of AI into PN enables highly personalized dietary recommendations by accounting for metabolic variability, genetics, and microbiome data. AI-driven strategies show potential in managing conditions like obesity and diabetes through accurate predictions of individual dietary responses. However, ethical, regulatory, and practical challenges must be addressed to ensure safe, equitable, and effective application of AI in nutrition.
概述人工智能在精准营养方面的潜力。
对654项关于人工智能（AI）和精准营养（PN）的研究进行的关键词共现分析突出了随机森林和梯度提升等人工智能技术在改善个性化饮食建议方面的潜力。这些方法可解决胃肠道症状、体重管理和心血管代谢指标问题，尤其是在纳入肠道微生物群数据时。尽管前景广阔，但数据隐私、偏差和伦理问题等挑战依然存在。人工智能必须辅助医疗保健专业人员，这需要明确的指导方针、强有力的管理和持续的研究，以确保安全有效的应用。将人工智能整合到精准营养中，通过考虑代谢变异性、遗传学和微生物组数据，能够实现高度个性化的饮食建议。人工智能驱动的策略通过准确预测个体饮食反应，在管理肥胖和糖尿病等病症方面显示出潜力。然而，必须解决伦理、监管和实际挑战，以确保人工智能在营养领域的安全、公平和有效应用。
