REVIEW article
Front. Nutr.
Sec. Nutrition Methodology
Volume 12 - 2025 | doi: 10.3389/fnut.2025.1635682
Large language models in clinical nutrition: an overview of its applications, capabilities, limitations, and potential future prospects
Provisionally accepted
Jamal Belkhouribchia1*
Joeri Jan Pen2- 1Endocrinology Center Hasselt, Hasselt, Belgium
- 2Universitair Ziekenhuis Brussel, Brussels, Belgium
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The integration of large language models (LLMs) into clinical nutrition marks a transformative advancement, offering promising solutions for enhancing patient care, personalizing dietary recommendations, and supporting evidence-based clinical decisionmaking. Trained on extensive text corpora and powered by transformer-based architectures, LLMs demonstrate remarkable capabilities in natural language understanding and generation.This review provides an overview of their current and potential applications in clinical nutrition, focusing on key technologies including prompt engineering, fine-tuning, retrievalaugmented generation, and multimodal integration. These enhancements increase domain relevance, factual accuracy, and contextual responsiveness, enabling LLMs to deliver more reliable outputs in nutrition-related tasks.Recent studies have shown LLMs' utility in dietary planning, nutritional education, obesity management, and malnutrition risk assessment. Despite these advances, challenges remain.Limitations in reasoning, factual accuracy, and domain specificity, along with risks of bias and hallucination, underscore the need for rigorous validation and human oversight. Furthermore, ethical considerations, environmental costs, and infrastructural integration must be addressed before widespread adoption.Future directions include combining LLMs with predictive analytics, integrating them with electronic health records and wearables, and adapting them for multilingual, culturally sensitive dietary guidance. LLMs also hold potential as research and educational tools, assisting in literature synthesis and patient engagement. Their transformative promise depends on cross-disciplinary collaboration, responsible deployment, and clinician training.Ultimately, while LLMs are not a replacement for healthcare professionals, they offer powerful augmentation tools for delivering scalable, personalized, and data-driven nutritional care in an increasingly complex healthcare environment.
Keywords: Large language models, Clinical Nutrition, artificial intelligence, personalized nutrition therapy, personalized dietary recommendations, retrieval-augmented generation large language models
Received: 27 May 2025; Accepted: 23 Jul 2025.
Copyright: © 2025 Belkhouribchia and Pen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jamal Belkhouribchia, Endocrinology Center Hasselt, Hasselt, Belgium
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.