使用人工智能辅助的饮食评估工具导航下一代营养护理 - - 对潜在应用的范围审查
Anuja Phalle1, Devaki Gokhale1
1Department of Nutrition and Dietetics, Symbiosis School of Culinary Arts and Nutritional Sciences, Symbiosis International (Deemed) University, Pune, India.
Frontiers in nutrition
|February 7, 2025
概括
人工智能 (AI) 和机器学习 (ML) 工具提供准确,用户友好的饮食评估,克服传统方法的局限性. 这些由人工智能驱动的解决方案通过减少偏见和改进数据收集以实现个性化营养来增强营养护理的提供.
科学领域:
- 营养科学 营养科学
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 传统的饮食评估是劳动密集型的,容易回忆偏差.
- 人工智能 (AI) 和机器学习 (ML) 的最新进展提供了潜在的解决方案.
- 人工智能辅助工具承诺用户友好和准确的饮食数据收集.
研究的目的:
- 审查人工智能辅助饮食评估工具的现实应用.
- 探索这些工具如何增强下一代营养护理.
- 确定当前人工智能饮食评估技术的功能和局限性.
主要方法:
- 在Scopus,Web of Science和PubMed数据库的系统文献搜索 (2014年1月至2024年9月).
- 关键词包括"人工智能"",食品图像分析"",可穿戴设备"",饮食评估"和"营养评估".
- 包括与人类参与者的研究;不包括方法/开发研究;遵循PRISMA-ScR指南.
主要成果:
- 人工智能工具集成到移动/网络应用程序中,分为基于图像或基于运动传感器的类别.
- 基于图像的工具进行食品识别,分类和营养估计.
- 基于运动传感器的工具通过运动和声音捕捉饮食场合,提供摄入量,份量大小和频率的实时数据.
- AI/ML工具准确地估计了能量和宏观营养素摄入量,减少了诸如肥胖和糖尿病等慢性疾病的回忆偏差.
- 这些工具是非劳动,时间效率和用户友好的,使个性化的营养.
结论:
- 整合基于AI的饮食评估工具可以显著提高营养护理质量.
- 这些工具对于推进下一代营养护理实践至关重要.
- 需要进一步的研究来验证这些新兴技术的有效性和准确性.
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