Herzlia Medical Center, Intensive Care Unit, Herzlia.
Critical Care Department and Institute for Nutrition Research, Rabin Medical Center, Beilinson Hospital, affiliated to the Sackler School of Medicine, Tel Aviv University, Tel Aviv.
Curr Opin Clin Nutr Metab Care. 2024 Mar 1;27(2):200-206. doi: 10.1097/MCO.0000000000000977. Epub 2023 Aug 29.
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.
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.
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.
人工智能已进入临床营养领域。为了实现个性化医疗，有许多工具可以使用。在这篇综述中，我们描述了医生如何利用不断增长的医疗保健数据库来开发深度学习和机器学习算法，从而帮助改善与临床营养相关的临床事件和结局的筛查、评估和预测。
人工智能可应用于临床营养的各个领域。利用大型数据库，已经可以改进筛查工具，识别营养不良的癌症患者或肥胖症患者。在重症监护中，机器学习已经能够预测肠内喂养不耐受、腹泻或再喂养低磷血症。癌症患者的结局也得到了改善。使用机器学习可以更好地将微生物组和代谢组学谱与临床情况相结合。然而，应该考虑人工智能的使用的伦理问题和局限性。
人工智能在这里是为了支持卫生专业人员的决策过程。了解其局限性和优势不仅将有助于临床营养领域，也将有助于整个医疗实践中的精准医疗。
