{"title":"营养不良中的人工智能:系统文献综述。","authors":"","doi":"10.1016/j.advnut.2024.100264","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":7349,"journal":{"name":"Advances in Nutrition","volume":null,"pages":null},"PeriodicalIF":8.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S216183132400098X/pdfft?md5=53a7e5b8137242198e4d8f55a968a21c&pid=1-s2.0-S216183132400098X-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence in Malnutrition: A Systematic Literature Review\",\"authors\":\"\",\"doi\":\"10.1016/j.advnut.2024.100264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":7349,\"journal\":{\"name\":\"Advances in Nutrition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.0000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S216183132400098X/pdfft?md5=53a7e5b8137242198e4d8f55a968a21c&pid=1-s2.0-S216183132400098X-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Nutrition\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S216183132400098X\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NUTRITION & DIETETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Nutrition","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S216183132400098X","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
Artificial Intelligence in Malnutrition: A Systematic Literature Review
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.
期刊介绍:
Advances in Nutrition (AN/Adv Nutr) publishes focused reviews on pivotal findings and recent research across all domains relevant to nutritional scientists and biomedical researchers. This encompasses nutrition-related research spanning biochemical, molecular, and genetic studies using experimental animal models, domestic animals, and human subjects. The journal also emphasizes clinical nutrition, epidemiology and public health, and nutrition education. Review articles concentrate on recent progress rather than broad historical developments.
In addition to review articles, AN includes Perspectives, Letters to the Editor, and supplements. Supplement proposals require pre-approval by the editor before submission. The journal features reports and position papers from the American Society for Nutrition, summaries of major government and foundation reports, and Nutrient Information briefs providing crucial details about dietary requirements, food sources, deficiencies, and other essential nutrient information. All submissions with scientific content undergo peer review by the Editors or their designees prior to acceptance for publication.