Artificial intelligence in clinical nutrition. A narrative review

Abstract

The rapid integration of Artificial Intelligence (AI) into healthcare, particularly clinical nutrition, holds transformative potential for enhanced diagnostics, risk prediction, and personalized therapeutic support. However, many clinicians lack sufficient understanding of AI principles, capabilities, and limitations, which may hinder adoption, lead to inappropriate use, or result in missed opportunities to enhance patient care. This narrative review aims to provide an accessible overview of foundational AI concepts, such as machine learning, deep learning, and large language models, and their practical applications in clinical nutrition. While AI offers immense promise in advancing nutritional care, its successful implementation requires clinicians to be adequately prepared to engage with these technologies. Education programs tailored to healthcare professionals, interdisciplinary collaboration between AI experts and clinicians, and robust ethical oversight are critical to ensure responsible and effective integration. By equipping clinicians with the necessary knowledge and tools, AI can serve as a powerful ally in delivering personalized and patient-centered nutritional care while maintaining human expertise at the forefront of decision-making. (c) 2025 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.