The future of artificial intelligence in clinical nutrition

Pierre Singer, Eyal Robinson, Orit Raphaeli

Research output: Contribution to journalReview articlepeer-review

Abstract

Purpose of reviewArtificial 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 findingsArtificial 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.SummaryArtificial 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.

Original languageEnglish
Pages (from-to)200-206
Number of pages7
JournalCurrent Opinion in Clinical Nutrition and Metabolic Care
Volume27
Issue number2
DOIs
StatePublished - 1 Mar 2024

Keywords

  • artificial intelligence
  • clinical nutrition
  • intensive care
  • machine learning

Fingerprint

Dive into the research topics of 'The future of artificial intelligence in clinical nutrition'. Together they form a unique fingerprint.

Cite this