Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are promising tools that can be used to develop algorithms to better understand and predict interactions between food- and nutrition-related data and health outcomes. Understanding that additional research is needed to identify areas where AI/ML is likely to have an impact, the National Academies Food and Nutrition Board hosted a public workshop in October 2023 to explore the future benefits and limitations of integrating big data and AI/ML tools into nutrition research. Participants also discussed issues related to diversity, equity, inclusion, bias, and privacy and the appropriate use of evidence generated from these new methods.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are promising tools that can be used to develop algorithms to better understand and predict interactions between food- and nutrition-related data and health outcomes. Understanding that additional research is needed to identify areas where AI/ML is likely to have an impact, the National Academies Food and Nutrition Board hosted a public workshop in October 2023 to explore the future benefits and limitations of integrating big data and AI/ML tools into nutrition research. Participants also discussed issues related to diversity, equity, inclusion, bias, and privacy and the appropriate use of evidence generated from these new methods.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are promising tools that can be used to develop algorithms to better understand and predict interactions between food- and nutrition-related data and health outcomes. Understanding that additional research is needed to identify areas where AI/ML is likely to have an impact, the National Academies Food and Nutrition Board hosted a public workshop in October 2023 to explore the future benefits and limitations of integrating big data and AI/ML tools into nutrition research. Participants also discussed issues related to diversity, equity, inclusion, bias, and privacy and the appropriate use of evidence generated from these new methods.