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Purpose of Review <p>This review explores how artificial intelligence can help advance clinical nutrition and address nutrition education and practice challenges. It highlights the role of AI, mainly through advanced clinical decision-making using generative AI, in supporting clinicians as they develop personalized nutrition interventions for individual patients. Furthermore, the review discusses how AI technologies are helping to close the knowledge gap in nutrition and delivering real-time, evidence-based insights to healthcare professionals.</p> Recent Findings <p>AI processes, such as machine learning and natural language processing, have shown promising results in predicting nutritional outcomes and complications, such as malnutrition and central line-associated bloodstream infections. Studies highlight the capability of AI to efficiently process large datasets, identify key risk factors, and provide real-time support to clinicians. Furthermore, AI can personalize educational content, making complex nutritional concepts more accessible.</p> Summary <p>AI has demonstrated multiple potential use cases in nutrition. However, much work still needs to be done to evaluate its accuracy, accessibility and ethical considerations.</p>

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Artificial Intelligence in Clinical Nutrition: Bridging Data Analytics and Nutritional Care

  • 期刊论文

  • 发表日期：

    2025年7月3日

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  • Jithinraj Edakkanambeth Varayil,
  • Suzette J. Bielinski,
  • Manpreet S. Mundi,
  • Sara L. Bonnes,
  • Bradley R. Salonen,
  • Ryan T. Hurt

摘要

Purpose of Review

This review explores how artificial intelligence can help advance clinical nutrition and address nutrition education and practice challenges. It highlights the role of AI, mainly through advanced clinical decision-making using generative AI, in supporting clinicians as they develop personalized nutrition interventions for individual patients. Furthermore, the review discusses how AI technologies are helping to close the knowledge gap in nutrition and delivering real-time, evidence-based insights to healthcare professionals.

Recent Findings

AI processes, such as machine learning and natural language processing, have shown promising results in predicting nutritional outcomes and complications, such as malnutrition and central line-associated bloodstream infections. Studies highlight the capability of AI to efficiently process large datasets, identify key risk factors, and provide real-time support to clinicians. Furthermore, AI can personalize educational content, making complex nutritional concepts more accessible.

Summary

AI has demonstrated multiple potential use cases in nutrition. However, much work still needs to be done to evaluate its accuracy, accessibility and ethical considerations.