{ "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.", "abstract_shown": true, "affiliations": [ "Cornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA", "Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA", "Department of Pediatrics, University of California San Diego, La Jolla, CA, USA", "Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA", "Program in International Nutrition, Cornell University, Ithaca, NY, USA", "Department of Computer Science, Cornell University, Ithaca, NY, USA", "Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA", "Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, USA", "RTI International, Durham, NC, USA", "Division of Health Informatics, Weill Cornell Medicine, New York City, NY, USA", "Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA", "Ann S. Bowers College of Computer and Information Science, Cornell University, Ithaca, NY, USA", "Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, NY, USA", "Institute for Computational Biomedicine, Weill Cornell Medicine, New York City, NY, USA", "Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu", "Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu", "Department of Pediatrics, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu", "Halicioğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu", "Hong Kong University of Science and Technology Jockey Club Institute for Advanced Study, Hong Kong SAR, China. rknight@ucsd.edu", "Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. rknight@ucsd.edu", "Cornell Joan Klein Jacobs Center for Precision Nutrition and Health, Cornell University, Ithaca, NY, USA. smehta@cornell.edu", "Department of Population Health Sciences, Weill Cornell Medicine, New York City, NY, USA. smehta@cornell.edu", "Division of Medical Informatics, St. John's Research Institute, Bengaluru, India. smehta@cornell.edu", "Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA. smehta@cornell.edu", "Program in International Nutrition, Cornell University, Ithaca, NY, USA. smehta@cornell.edu" ], "article_type": "review", "arxiv": null, "author_total": 15, "authors": [ { "affs": [ 0 ], "name": "Paraskevi Massara", "orcid": "0000-0003-0906-0985" }, { "affs": [ 1, 2 ], "name": "Jonathan Kirkland", "orcid": "0009-0001-6633-1844" }, { "affs": [ 0 ], "name": "Ioanna Pagani" }, { "affs": [ 0, 3, 4 ], "name": "Samantha Lee Huey", "orcid": "0000-0002-9774-8642" }, { "affs": [ 0, 5 ], "name": "Haym Hirsh" }, { "affs": [ 2 ], "name": "Daniel T. McDonald", "orcid": "0000-0003-0876-9060" }, { "affs": [ 1, 2, 6 ], "name": "Lucas Patel", "orcid": "0000-0001-8607-2782" }, { "affs": [ 0, 7, 3, 4 ], "name": "Julia Leigh Finkelstein", "orcid": "0000-0001-9512-5559" }, { "affs": [ 8 ], "name": "Marie G. Gantz", "orcid": "0000-0001-8528-0184" }, { "affs": [ 0, 9 ], "name": "Fei Wang", "orcid": "0000-0001-9459-9461" }, { "affs": [ 0, 10 ], "name": "David Erickson" }, { "affs": [ 11, 0 ], "name": "Martin T. Wells" }, { "affs": [ 12, 0, 13 ], "name": "Olivier Elemento", "orcid": "0000-0002-8061-9617" }, { "affs": [ 14, 15, 16, 17, 18, 19 ], "name": "Rob D. Knight", "orcid": "0000-0002-0975-9019" }, { "affs": [ 20, 21, 22, 23, 24 ], "name": "Saurabh Mehta", "orcid": "0000-0003-3788-0665" } ], "bibtex": "https://research.draconitic.com/paper/3rjnkyzhjkjp0.bib", "cited_by": 15, "doi": "10.1038/s41467-026-75004-w", "fulltext_url": null, "id": "3rjnkyzhjkjp0", "issns": [ "2041-1723" ], "landing_url": "https://doi.org/10.1038/s41467-026-75004-w", "language": "en", "licence": "CC-BY-NC-ND", "licence_label": "CC BY-NC-ND", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1311|Nutrition, Genetics, and Disease" ] ], "openalex": "W7167507847", "pdf_url": "https://www.nature.com/articles/s41467-026-75004-w.pdf", "pmcid": "PMC13338271", "pmid": "42409828", "published": "2026-07-06", "publisher": "Nature Portfolio", "ris": "https://research.draconitic.com/paper/3rjnkyzhjkjp0.ris", "sources": [ { "first_seen": "2026-10-30T13:26:46Z", "native_id": "W7167507847", "source": "openalex", "url": "https://doi.org/10.1038/s41467-026-75004-w" } ], "subjects": [ { "field": "biology", "origin": "openalex:1311|Nutrition, Genetics, and Disease", "subfield": "genetics-genomics" } ], "title": "Applying Artificial Intelligence and machine learning in precision nutrition", "title_text": "Applying Artificial Intelligence and machine learning in precision nutrition", "type": "peer-reviewed", "updated_at": "2026-11-09T13:21:05Z", "url": "https://research.draconitic.com/paper/3rjnkyzhjkjp0", "venue": "Nature Communications", "versions": [] }