Abstract：
Malnutrition among the population of the world is a frequent yet underdiagnosed problem in both children and adults. Development of malnutrition screening and diagnostic tools for early detection of malnutrition is necessary to prevent long-term complications to patients\' health and well-being. Most of these tools are based on predefined questionnaires and consensus guidelines. The use of artificial intelligence (AI) allows for automated tools to detect malnutrition in an earlier stage to prevent long-term consequences. In this study, a systematic literature review was carried out with the goal of providing detailed information on what patient groups, screening tools, machine learning algorithms, data types, and variables are being used, as well as the current limitations and implementation stage of these AI-based tools. The results showed that a staggering majority exceeding 90% of all AI models go unused in day-to-day clinical practice. Furthermore, supervised learning models seemed to be the most popular type of learning. Alongside this, disease-related malnutrition was the most common category of malnutrition found in the analysis of all primary studies. This research provides a resource for researchers to identify directions for their research on the use of AI in malnutrition.
摘要：
世界人口中的营养不良是儿童和成人中常见但未被诊断的问题。开发营养不良筛查和早期发现营养不良的诊断工具对于预防患者健康和福祉的长期并发症是必要的。这些工具大多数基于预定义的问卷和共识准则。人工智能（AI）的使用允许自动化工具在早期阶段检测营养不良，以防止长期后果。在这项研究中,进行了系统的文献综述，目的是提供关于哪些患者组的详细信息，筛选工具,机器学习算法，数据类型,和变量正在使用，以及这些基于AI的工具的当前限制和实施阶段。结果显示，超过90%的AI模型在日常临床实践中没有使用。此外，监督学习模型似乎是最受欢迎的学习类型。除此之外,疾病相关营养不良是所有主要研究分析中发现的最常见的营养不良类别.当前的研究为研究人员提供了资源，以确定他们在营养不良中使用AI的研究方向。
