DietAI24 as a framework for comprehensive nutrition estimation using multimodal large language models
DietAI24 demonstrates that integrating MLLMs with RAG and standardized nutrition databases can substantially improve dietary assessment accuracy while enabling comprehensive nutrient analysis. This framework offers a scalable solution for nutrition research and clinical applications, potentially transforming large-scale epidemiological studies and personalized dietary interventions through more accurate and less burdensome dietary data collection.
Authors
- Carl Yang (ORCID: https://orcid.org/0000-0001-9145-4531)
- Hong Qin (ORCID: https://orcid.org/0000-0003-0304-3762)
- Darren Liu (ORCID: https://orcid.org/0009-0004-5019-4402)
- Terry Hartman
- Hanqi Luo (ORCID: https://orcid.org/0000-0001-6253-5818)
- Runze Yan (ORCID: https://orcid.org/0000-0002-6558-4567)
- Janice MacLeod (ORCID: https://orcid.org/0000-0001-8689-4938)
- Jiaying Lu (ORCID: https://orcid.org/0000-0001-9052-6951)
- Xiao Hu (ORCID: https://orcid.org/0000-0001-9478-5571)
- Mehak Preet Dhaliwal
- Hannah Posluszny
Institutions
- Emory University (US)
- University of California, Santa Barbara (US)
- Parents' Place of Maryland (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1038/s43856-025-01159-0
- Citations
- 5
- Primary Topic
- Nutrition, Genetics, and Disease
- Type
- article
- Field-Weighted Citation Impact
- 9.09