AI in Dietary Intake Assessment: Smarter Nutrition Decisions
AI in dietary intake assessment is changing how people understand their eating habits and make food choices. By analysing dietary information more efficiently and providing personalised recommendations, artificial intelligence has the potential to support healthier lifestyles while making nutrition guidance more accessible.
Could AI help people make better food decisions every day? And how can we ensure that these recommendations are both accurate and trustworthy? This article explores the growing role of AI in dietary intake assessment, the challenges that still need to be addressed and how WiseFood is developing solutions that combine advanced technology with expert knowledge.
Why Dietary Intake Assessment Matters
Dietary intake assessment plays an important role in understanding eating habits, supporting healthier lifestyles and informing nutrition research and public health policies. However, accurately measuring what people eat has never been straightforward.
Traditional methods often rely on food diaries or 24 hour recalls, which depend on memory and accurate portion size estimation. People may unintentionally forget foods they consumed or misjudge quantities, affecting the quality of the collected data.
Artificial intelligence offers an opportunity to improve this process by analysing dietary information more efficiently and providing support that adapts to each individual’s needs.
How AI Is Improving Dietary Intake Assessment
Artificial intelligence can analyse multiple sources of information, including dietary preferences, eating habits, lifestyle and individual health characteristics, to generate personalised nutrition recommendations. Unlike traditional approaches that often provide general guidance, AI can continuously adapt its suggestions as new information becomes available.
This makes dietary assessment more responsive to individual needs while reducing some of the effort associated with manually recording and analysing food intake. AI can also assist nutrition professionals by recognising dietary patterns that may otherwise be difficult to identify.
Beyond personalised recommendations, AI is increasingly supporting nutrition research, food safety monitoring, data analysis and evidence based decision making. As these technologies continue to develop, they have the potential to improve both research and everyday dietary guidance.
Why Human Expertise Still Matters
Research has shown that AI generated dietary recommendations can closely follow established nutrition guidelines while providing advice tailored to different user needs. At the same time, studies also demonstrate that there is still room for improvement.
Some recommendations may not fully reflect individual dietary restrictions or personal preferences, while nutrient estimates can occasionally fall outside recommended ranges. These findings show why continuous testing, validation and expert oversight remain essential.
Validation studies of AI assisted dietary assessment tools have produced encouraging results. One study found that an AI supported mobile application estimated nutrient intake at least as accurately as traditional 24 hour dietary recalls, while also identifying opportunities to improve food recognition and portion size estimation.
Rather than replacing nutrition professionals, AI works best as a decision support tool that combines data driven analysis with human expertise.
How WiseFood Addresses These Challenges
WiseFood has been developed with these opportunities and challenges in mind. The project aims to support healthier and more sustainable food choices through AI powered tools that are practical, reliable and easy to use.
Instead of offering a single solution, WiseFood provides three complementary applications designed to support different aspects of everyday food decisions.
- FoodScholar helps users explore trustworthy information about nutrition, health and sustainability through an intuitive natural language interface.
- RecipeWrangler analyses recipes by evaluating both their nutritional value and environmental impact while suggesting healthier and more sustainable alternatives.
- FoodChat creates personalised meal plans and recipe recommendations that encourage healthier eating while helping users reduce food waste.
The applications are designed for everyday use without requiring technical expertise. They also reflect national nutrition and sustainability standards, helping users make informed decisions based on reliable information.
The Technology Behind WiseFood
WiseFood combines several artificial intelligence technologies to deliver recommendations that users can trust.
Knowledge Graphs organise reliable food related information, making complex nutrition knowledge easier to understand and navigate.
Large Language Models (LLMs) enable personalised interactions, helping users receive recommendations that reflect their individual needs and preferences.
Most importantly, WiseFood follows a human in the loop approach. Nutrition experts remain actively involved in developing, validating and refining the AI generated recommendations, ensuring they remain accurate, ethical and aligned with scientific evidence and public policies.
This combination of advanced technology and expert guidance directly addresses many of the challenges identified in current AI nutrition research.
To stay up to date with the latest WiseFood developments, follow the project on LinkedIn and visit the WiseFood Newsroom for research highlights, project news and practical examples of the technologies being developed.
References
Hart KH, Wilson-Barnes S, Stefanidis K, et al. The suitability of dietary recommendations suggested By artificial intelligence technology via a novel personalised nutrition mobile application. Proceedings of the Nutrition Society. 2022;81(OCE1):E37. doi:10.1017/S0029665122000374
Folson, G. K., Bannerman, B., Atadze, V., Ador, G., Kolt, B., McCloskey, P., Gangupantulu, R., Arrieta, A., Braga, B. C., Arsenault, J., Kehs, A., Doyle, F., Tran, L. M., Hoang, N. T., Hughes, D., Nguyen, P. H., & Gelli, A. (2023). Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana. The Journal of Nutrition, 153(8), 2328-2338. https://doi.org/10.1016/j.tjnut.2023.06.001
AI Applications for Nutrition and Food Security Research: A Taxonomy and Competencies (2025). Nutrition Today, 60(1), E1-E1. https://doi.org/10.1097/NT.0000000000000735