ArticleNutrition & diabetes2022
Machine learning modeling practices to support the principles of AI and ethics in nutrition research.
Article in Nutrition & diabetes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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Who cites it
15 citing papers in PubMed.
- Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.The Journal of nutrition · 2026Review
- Review
- Artificial Intelligence-Guided Artificial Nutrition in Critical Illness: Integrating Indirect Calorimetry and BIVA for Metabolic Precision.Nutrients · 2026Review
- Simulation of the Metabolic Response to an Interventional Study with New Healthy Beverages by Machine-Learning Regression.Journal of agricultural and food chemistry · 2026Article
- AI-Enabled Precision Nutrition in the ICU: A Narrative Review and Implementation Roadmap.Nutrients · 2025Review
- Leveraging Deep Learning to Enhance Malnutrition Detection via Nutrition Risk Screening 2002: Insights from a National Cohort.Nutrients · 2025Article
- Benchmarking Missing Data Imputation Methods for Time Series Using Real-World Test Cases.Proceedings of machine learning research · 2025Article
- Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.Nutrients · 2025Review
- The anthropomorphization of AI and the concept of Buddhist compassion in human-machine interaction.Frontiers in psychology · 2025Article
- Optimization of diabetes prediction methods based on combinatorial balancing algorithm.Nutrition & diabetes · 2024Review
- Article
- Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass.European journal of clinical nutrition · 2024Article
- Transforming Big Data into AI-ready data for nutrition and obesity research.Obesity (Silver Spring, Md.) · 2024Review
- Artificial Intelligence Applications to Public Health Nutrition.Nutrients · 2023Article
- Explaining sentiment analysis results on social media texts through visualization.Multimedia tools and applications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundNutrition 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.
methodsBased 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.
resultsHere, 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.
conclusionThe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.