Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions
Artificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of AI-driven innovations that enable preci...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Nutrition |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnut.2025.1636980/full |
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| author | Kushagra Agrawal Polat Goktas Navneet Kumar Man-Fai Leung |
| author_facet | Kushagra Agrawal Polat Goktas Navneet Kumar Man-Fai Leung |
| author_sort | Kushagra Agrawal |
| collection | DOAJ |
| description | Artificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of AI-driven innovations that enable precision nutrition through real-time dietary recommendations, meal planning informed by individual biological markers (e.g., blood glucose or cholesterol levels), and adaptive feedback systems. It further examines the integration of AI technologies in food production, such as machine learning–based quality control, predictive maintenance, and waste minimization, to support circular economy goals and enhance food system resilience. Drawing on advances in deep learning, federated learning, and computer vision, the review outlines how AI transforms static, population-level dietary models into dynamic, data-informed frameworks tailored to individual needs. The paper also addresses critical challenges related to algorithmic transparency, data privacy, and equitable access, and proposes actionable pathways for ethical and scalable implementation. By bridging healthcare, nutrition, and industrial domains, this study offers a forward-looking roadmap for leveraging AI to build intelligent, inclusive, and sustainable food–health ecosystems. |
| format | Article |
| id | doaj-art-e6c987162fe141a7b19243d6d8a212dc |
| institution | Kabale University |
| issn | 2296-861X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Nutrition |
| spelling | doaj-art-e6c987162fe141a7b19243d6d8a212dc2025-08-20T03:25:29ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-07-011210.3389/fnut.2025.16369801636980Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directionsKushagra Agrawal0Polat Goktas1Navneet Kumar2Man-Fai Leung3School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, IndiaUCD School of Computer Science, University College Dublin, Dublin, IrelandESM Division, ICAR - National Academy of Agricultural Research Management, Hyderabad, IndiaSchool of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United KingdomArtificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of AI-driven innovations that enable precision nutrition through real-time dietary recommendations, meal planning informed by individual biological markers (e.g., blood glucose or cholesterol levels), and adaptive feedback systems. It further examines the integration of AI technologies in food production, such as machine learning–based quality control, predictive maintenance, and waste minimization, to support circular economy goals and enhance food system resilience. Drawing on advances in deep learning, federated learning, and computer vision, the review outlines how AI transforms static, population-level dietary models into dynamic, data-informed frameworks tailored to individual needs. The paper also addresses critical challenges related to algorithmic transparency, data privacy, and equitable access, and proposes actionable pathways for ethical and scalable implementation. By bridging healthcare, nutrition, and industrial domains, this study offers a forward-looking roadmap for leveraging AI to build intelligent, inclusive, and sustainable food–health ecosystems.https://www.frontiersin.org/articles/10.3389/fnut.2025.1636980/fullartificial intelligencepersonalized nutritionfood manufacturingmachine learningfederated learningpredictive analytics |
| spellingShingle | Kushagra Agrawal Polat Goktas Navneet Kumar Man-Fai Leung Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions Frontiers in Nutrition artificial intelligence personalized nutrition food manufacturing machine learning federated learning predictive analytics |
| title | Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions |
| title_full | Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions |
| title_fullStr | Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions |
| title_full_unstemmed | Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions |
| title_short | Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions |
| title_sort | artificial intelligence in personalized nutrition and food manufacturing a comprehensive review of methods applications and future directions |
| topic | artificial intelligence personalized nutrition food manufacturing machine learning federated learning predictive analytics |
| url | https://www.frontiersin.org/articles/10.3389/fnut.2025.1636980/full |
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