{"title":"The future of artificial intelligence in clinical nutrition.","authors":"Pierre Singer, Eyal Robinson, Orit Raphaeli","doi":"10.1097/MCO.0000000000000977","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose of review: </strong>Artificial intelligence has reached the clinical nutrition field. To perform personalized medicine, numerous tools can be used. In this review, we describe how the physician can utilize the growing healthcare databases to develop deep learning and machine learning algorithms, thus helping to improve screening, assessment, prediction of clinical events and outcomes related to clinical nutrition.</p><p><strong>Recent findings: </strong>Artificial intelligence can be applied to all the fields of clinical nutrition. Improving screening tools, identifying malnourished cancer patients or obesity using large databases has been achieved. In intensive care, machine learning has been able to predict enteral feeding intolerance, diarrhea, or refeeding hypophosphatemia. The outcome of patients with cancer can also be improved. Microbiota and metabolomics profiles are better integrated with the clinical condition using machine learning. However, ethical considerations and limitations of the use of artificial intelligence should be considered.</p><p><strong>Summary: </strong>Artificial intelligence is here to support the decision-making process of health professionals. Knowing not only its limitations but also its power will allow precision medicine in clinical nutrition as well as in the rest of the medical practice.</p>","PeriodicalId":10962,"journal":{"name":"Current Opinion in Clinical Nutrition and Metabolic Care","volume":" ","pages":"200-206"},"PeriodicalIF":3.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Clinical Nutrition and Metabolic Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/MCO.0000000000000977","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/29 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose of review: Artificial intelligence has reached the clinical nutrition field. To perform personalized medicine, numerous tools can be used. In this review, we describe how the physician can utilize the growing healthcare databases to develop deep learning and machine learning algorithms, thus helping to improve screening, assessment, prediction of clinical events and outcomes related to clinical nutrition.
Recent findings: Artificial intelligence can be applied to all the fields of clinical nutrition. Improving screening tools, identifying malnourished cancer patients or obesity using large databases has been achieved. In intensive care, machine learning has been able to predict enteral feeding intolerance, diarrhea, or refeeding hypophosphatemia. The outcome of patients with cancer can also be improved. Microbiota and metabolomics profiles are better integrated with the clinical condition using machine learning. However, ethical considerations and limitations of the use of artificial intelligence should be considered.
Summary: Artificial intelligence is here to support the decision-making process of health professionals. Knowing not only its limitations but also its power will allow precision medicine in clinical nutrition as well as in the rest of the medical practice.
期刊介绍:
A high impact review journal which boasts an international readership, Current Opinion in Clinical Nutrition and Metabolic Care offers a broad-based perspective on the most recent and exciting developments within the field of clinical nutrition and metabolic care. Published bimonthly, each issue features insightful editorials and high quality invited reviews covering two or three key disciplines which include protein, amino acid metabolism and therapy, lipid metabolism and therapy, nutrition and the intensive care unit and carbohydrates. Each discipline introduces world renowned guest editors to ensure the journal is at the forefront of knowledge development and delivers balanced, expert assessments of advances from the previous year.