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人工智能,机器学习和深度学习在营养方面的应用:系统性审查
Tagne Poupi Theodore Armand1, Kintoh Allen Nfor2, Jung-In Kim1
1Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea.
Nutrients
|April 13, 2024
概括
人工智能 (AI) 正在通过实现智能,个性化的饮食计划和疾病预测来改变营养. 本综述探讨了人工智能,机器学习 (ML) 和深度学习 (DL) 在营养科学中的应用.
科学领域:
- 营养科学 营养科学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 工业4.0强调自动化和数字化.
- 人工智能 (AI) 提供跨领域的创新解决方案.
- 营养是受人工智能影响的关键公共卫生领域.
研究的目的:
- 调查人工智能在营养领域的现状.
- 了解AI,机器学习 (ML) 和深度学习 (DL) 在营养方面的潜力.
- 强调AI在营养方面的挑战和未来方向.
主要方法:
- 混合系统文献审查 (SLR) 和PRISMA指南.
- 在主要的科学数据库中搜索营养文献中的AI.
- 严格的研究选择和方法质量评估.
主要成果:
- 在个性化营养,饮食评估,食品跟踪和疾病预测/监测方面确定了人工智能应用.
- 在分析复杂的营养数据方面证明了ML和DL的多功能性.
- 强调AI在智能营养和饮食建议中的作用.
结论:
- 人工智能在营养方面的整合显示出改善个体结果的重大前景.
- 人工智能可以优化饮食建议,并支持基于证据的决策.
- 这项研究有助于专业人员设计未来的AI驱动营养项目.
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