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营养不良中的人工智能:一个系统的文献综述
Sander Mw Janssen1, Yamine Bouzembrak1, Bedir Tekinerdogan1
1Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands.
Advances in nutrition (Bethesda, Md.)
|July 6, 2024
概括
人工智能 (AI) 可以帮助早期发现营养不良. 然而,超过90%的人工智能模型用于营养不良查仍未在临床实践中使用,这突出了重大实施差距.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 营养科学 营养科学
背景情况:
- 营养不良是一个普遍的,未被确诊的全球健康问题,影响儿童和成年人.
- 早期检测和干预对于预防严重的长期健康并发症至关重要.
- 目前的营养不良查工具通常依赖于问卷和共识指南.
研究的目的:
- 系统地审查人工智能 (AI) 应用程序用于营养不良的检测.
- 为了识别患者群体,查工具,机器学习算法,数据类型和用于基于AI的营养不良检测的变量.
- 评估AI工具在临床实践中的局限性和实施状态.
主要方法:
- 基于人工智能的营养不良查和诊断工具的系统文献综述.
- 分析患者队列,人工智能方法,数据源和使用的变量.
- 评估已识别的AI工具的临床实施和局限性.
主要成果:
- 超过90%的营养不良人工智能模型尚未在日常临床实践中得到应用.
- 监督学习模型是最常用的机器学习方法.
- 疾病相关的营养不良是主要研究中分析的最常见的类别.
结论:
- 人工智能在早期发现营养不良方面具有显著的潜力,但在临床采用方面面临重大障碍.
- 需要进一步的研究来弥合人工智能模型开发和现实世界的临床实施之间的差距.
- 本综述是指导人工智能对营养不良的未来研究的资源.
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