Artificial intelligence in clinical nutrition. A narrative review.
Clinical Snapshot
PICO Framework
| P — Population | Healthcare professionals and clinicians working in clinical nutrition |
| I — Intervention | Educational overview of artificial intelligence applications in clinical nutrition |
| C — Comparator | Current clinical nutrition practice without AI integration |
| O — Outcomes | Understanding of AI concepts, applications, and implementation considerations in clinical nutrition |
Bottom Line
This narrative review provides a foundational educational resource for clinicians seeking to understand AI applications in clinical nutrition. While the review successfully identifies key AI concepts and potential applications, it lacks the systematic methodology needed for robust evidence synthesis. The emphasis on clinician education and ethical implementation is valuable, but the review would benefit from more critical analysis of implementation barriers, costs, and potential risks. For Australian practitioners, the concepts are broadly applicable, though specific regulatory and system integration considerations would need local adaptation. The review serves as a useful starting point for AI literacy in nutrition practice but should be supplemented with more rigorous systematic evidence when making implementation decisions.
Key Findings
Effect Size: Not applicable - educational review
Primary Outcome: Educational framework for AI understanding in clinical nutrition
Nnt Or Sensitivity: Not applicable - narrative review format
Clinical Application
High feasibility for educational implementation; variable for AI technology adoption depending on institutional resources Relevant to Australian practice given similar healthcare challenges; would need consideration of TGA regulations for AI medical devices and integration with existing clinical decision support systems Dietitians, clinical nutritionists, endocrinologists, and healthcare professionals involved in nutritional care
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.
References
- 1.Belkhouribchia, J., & Pen, J. J. (2026). Artificial intelligence in clinical nutrition. A narrative review. Clinical Nutrition ESPEN. https://doi.org/10.1016/j.clnesp.2025.11.142
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