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Peer-reviewedCC BY-NC-NDNature Communications

Applying Artificial Intelligence and machine learning in precision nutrition

  • Paraskevi Massara1
  • Jonathan Kirkland2,3
  • Ioanna Pagani1
  • Samantha Lee Huey1,4,5
  • Haym Hirsh1,6
  • Daniel T. McDonald3
  • Lucas Patel2,3,7
  • Julia Leigh Finkelstein1,8,4,5
  • Marie G. Gantz9
  • Fei Wang1,10
  • David Erickson1,11
  • Martin T. Wells12,1
3 more authors
  • Olivier Elemento13,1,14
  • Rob D. Knight15,16,17,18,19,20
  • Saurabh Mehta21,22,23,24,25
25 affiliations
  1. 1Cornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA
  2. 2Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
  3. 3Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
  4. 4Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA
  5. 5Program in International Nutrition, Cornell University, Ithaca, NY, USA
  6. 6Department of Computer Science, Cornell University, Ithaca, NY, USA
  7. 7Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA
  8. 8Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, USA
  9. 9RTI International, Durham, NC, USA
  10. 10Division of Health Informatics, Weill Cornell Medicine, New York City, NY, USA
  11. 11Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA
  12. 12Ann S. Bowers College of Computer and Information Science, Cornell University, Ithaca, NY, USA
  13. 13Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, NY, USA
  14. 14Institute for Computational Biomedicine, Weill Cornell Medicine, New York City, NY, USA
  15. 15Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA. [email protected]
  16. 16Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. [email protected]
  17. 17Department of Pediatrics, University of California San Diego, La Jolla, CA, USA. [email protected]
  18. 18Halicioğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA. [email protected]
  19. 19Hong Kong University of Science and Technology Jockey Club Institute for Advanced Study, Hong Kong SAR, China. [email protected]
  20. 20Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. [email protected]
  21. 21Cornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA. [email protected]
  22. 22Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, USA. [email protected]
  23. 23Division of Medical Informatics, St. John's Research Institute, Bengaluru, India. [email protected]
  24. 24Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA. [email protected]
  25. 25Program in International Nutrition, Cornell University, Ithaca, NY, USA. [email protected]
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Abstract

A key feature of the Precision Nutrition and Health approach is the ability to tailor interventions to individual variability using multimodal data from large-scale biobanks and cohorts. Artificial intelligence (AI) and machine learning (ML) models offer new potential to model complex data but remain constrained by challenges related to data quality, interpretability, validation, and causal inference. This Perspective synthesizes current AI/ML methodologies in PN, elucidates their interplay with the distinctive features of multi-omic and nutritional data, such as being compositional, episodic, context-dependent, and error-prone, and delineates nutrition-specific best practices for achieving robust, interpretable, and clinically actionable AI integration in research and practice.

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Cite

BibTeXRIS
BibTeX
@article{massara2026applying,
  title = {{Applying Artificial Intelligence and machine learning in precision nutrition}},
  author = {Paraskevi Massara and Jonathan Kirkland and Ioanna Pagani and Samantha Lee Huey and Haym Hirsh and Daniel T. McDonald and Lucas Patel and Julia Leigh Finkelstein and Marie G. Gantz and Fei Wang and David Erickson and Martin T. Wells and Olivier Elemento and Rob D. Knight and Saurabh Mehta},
  journal = {Nature Communications},
  year = {2026},
  month = jul,
  doi = {10.1038/s41467-026-75004-w},
  url = {https://doi.org/10.1038/s41467-026-75004-w},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Applying Artificial Intelligence and machine learning in precision nutrition
AU  - Paraskevi Massara
AU  - Jonathan Kirkland
AU  - Ioanna Pagani
AU  - Samantha Lee Huey
AU  - Haym Hirsh
AU  - Daniel T. McDonald
AU  - Lucas Patel
AU  - Julia Leigh Finkelstein
AU  - Marie G. Gantz
AU  - Fei Wang
AU  - David Erickson
AU  - Martin T. Wells
AU  - Olivier Elemento
AU  - Rob D. Knight
AU  - Saurabh Mehta
PY  - 2026
DA  - 2026/07/06
JO  - Nature Communications
DO  - 10.1038/s41467-026-75004-w
UR  - https://doi.org/10.1038/s41467-026-75004-w
AB  - A key feature of the Precision Nutrition and Health approach is the ability to tailor interventions to individual variability using multimodal data from large-scale biobanks and cohorts. Artificial intelligence (AI) and machine learning (ML) models offer new potential to model complex data but remain constrained by challenges related to data quality, interpretability, validation, and causal inference. This Perspective synthesizes current AI/ML methodologies in PN, elucidates their interplay with the distinctive features of multi-omic and nutritional data, such as being compositional, episodic, context-dependent, and error-prone, and delineates nutrition-specific best practices for achieving robust, interpretable, and clinically actionable AI integration in research and practice.
SN  - 2041-1723
ER  -