Artificial intelligence (AI) in nutrition: A case‐based comparison of generative AI models
LLM-based AIAs can reliably replicate expert reasoning in CN. Although not yet a source of novel clinical insights, the true potential of this approach may lie in its application among physicians without specialized expertise in CN, helping to bridge existing knowledge gaps in nutrition care. Presenting full clinical cases, as shown in this study, could support AIA-enabled e-consultation in the future, thereby addressing gaps in CN education.
Authors
- Ryan T. Hurt (ORCID: https://orcid.org/0000-0003-1158-7559)
- Ivana T. Croghan (ORCID: https://orcid.org/0000-0003-3464-3525)
- Bradley R. Salonen (ORCID: https://orcid.org/0000-0002-1981-5045)
- Sara L. Bonnes (ORCID: https://orcid.org/0000-0003-1488-3417)
- Manpreet S. Mundi (ORCID: https://orcid.org/0000-0001-7902-3544)
- Shawn C. Fokken
- Kalpana Muthusamy
- Jithinraj Edakkanambeth Varayil (ORCID: https://orcid.org/0000-0001-9738-6699)
- Chris R. Stephenson
Institutions
- Mayo Clinic (US)
- WinnMed (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Nutrition in Clinical Practice
- Published
- 2025-11-06
- DOI
- https://doi.org/10.1002/ncp.70062
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65
Funders
- National Institutes of Health
- National Center for Advancing Translational Sciences