人工智能驱动的个性化营养:整合Omics,伦理和数字健康
Celin Mundt1, Büşra Yusufoğlu2, Daniel Kudenko3
1Department of Molecular Food Chemistry and Food Development, Institute of Food and One Health, Gottfried Wilhelm Leibniz University Hannover, Hannover, Germany.
Molecular nutrition & food research
|October 30, 2025
概括
人工智能 (AI) 通过分析复杂的健康数据来增强个性化营养 (PN) 以用于慢性疾病管理. 这种方法扩展了传统的策略,整合了微生物组和奥米克数据,用于定制的饮食干预措施.
科学领域:
- 综合生物学和计算健康科学.
- 人工智能在营养科学和慢性疾病管理中的应用.
背景情况:
- 个性化营养 (PN) 旨在通过个人遗传,代谢和生活方式数据来预防和管理慢性疾病.
- 人工智能 (AI) 对于分析肥胖,糖尿病和心血管疾病等疾病的大型多组数据集至关重要.
研究的目的:
- 分析人工智能驱动的个性化营养研究的全球趋势和范围.
- 评估人工智能,微生物组数据和omics在扩大传统营养策略中的整合.
- 确定挑战,并提出解决方案,用于AI在PN的临床应用.
主要方法:
- 基于人工智能的个性化营养研究的文献分析分析.
- 对基于微生物组的饮食干预和肥胖管理中人工智能模型应用的审查.
- 在个性化干预中检查数字双胞胎和健康知识图.
主要成果:
- 全球基于人工智能的PN研究显著增加,由数字健康趋势和COVID-19大流行加速.
- 人工智能模型在指导基于微生物组的干预和支持肥胖管理方面表现出有效性.
- 通过机器学习扩展传统的营养策略,微生物组和omics集成.
结论:
- 人工智能有可能改变营养科学,为个人和社会健康提供预测性,适应性和基于伦理的方法.
- 通过各种数据集,可解释的人工智能和验证协议来解决算法偏差,概括性和数据隐私等挑战至关重要.
- 标准化的多中心验证是必要的,以将AI支持的PN转化为临床适用的框架.
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