Machine learning modeling practices to support the principles of AI and ethics in nutrition research
Open accessAbstract
BACKGROUND: Nutrition research is relying more on artificial intelligence and machine learning models to understand, diagnose, predict, and explain data. While artificial intelligence and machine learning models provide powerful modeling tools, failure to use careful and well-thought-out modeling processes can lead to misleading conclusions and concerns surrounding ethics and bias. METHODS: Based on our experience as reviewers and journal editors in nutrition and obesity, we identified the most frequently omitted best practices from statistical modeling and how these same practices extend to machine learning models. We next addressed areas required for implementation of machine learning that are not included in commercial software packages. RESULTS: Here, we provide a tutorial on best artificial intelligence and machine learning modeling practices that can reduce potential ethical problems with a checklist and guiding principles to aid nutrition researchers in developing, evaluating, and implementing artificial intelligence and machine learning models in nutrition research. CONCLUSION: The quality of AI/ML modeling in nutrition research requires iterative and tailored processes to mitigate against potential ethical problems or to predict conclusions that are free of bias.
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Predictive triage tools enable early identification of patients requiring urgent care, facilitating timely intervention.
“Predictive triage tools also enable early identification of patients requiring urgent care, facilitating timely intervention [39].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [39]
The Introduction (page unavailable) discusses machine learning for personalized nutrition, and the Missing data section discusses improving predictions from electronic health records. Neither addresses predictive triage, early identification of patients needing urgent care, or timely intervention. Searches for those terms found no relevant passage in our copy. (LLM, conf=0.72)
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- Authors
- Thomas, D. M., Kleinberg, S., Brown, A. W., Crow, M., Bastian, N. D., Reisweber, N., Lasater, R., Kendall, T., Shafto, P., Blaine, R., Smith, S., Ruiz, D., Morrell, C., & Clark, N.
- Affiliations
- United States Military Academy (US)
- Stevens Institute of Technology (US)
- Arkansas Children's Research Institute (US)
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Thomas, Diana M.
- ORCID
- Venue
- Nutrition & Diabetes
- Published
- 2022-12-02
- Volume
- 12
- Issue
- 1
- Pages
- 48-48
- ISSN
- 2044-4052 (ISSN-L)
- PMC
- PMC9715415
- OpenAlex
- W4310540378
- Cited by
- 48
- References
- 75
- Citations by year
- 2026: 14 · 2025: 17 · 2024: 12 · 2023: 5
- FWCI
- 9.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nutrition and Diabetes (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (NIH U54TR004279, U54TR004279, NIH R25DK099080, NIH R25GM141507, NIH R25HL124208)
- United States Department of Defense | Defense Advanced Research Projects Agency (XAI FA8750-17-2-0146)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, artificial intelligence, nutrition research, AI ethics, algorithmic bias, model evaluation, obesity research, research reproducibility, AI implementation
- MeSH terms
- Machine Learning, Artificial Intelligence, Humans, Nutritional Status, Obesity
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