The New Personalized AI Nutritionist
In recent years, artificial intelligence (AI) has witnessed an unprecedented acceleration in capabilities and commercial adoption, including a particularly impactful yet under-discussed area: the nutritional health industry. The emerging potential of AI to personalize nutrition advice is significantly altering how individuals can manage their food choices.1 These AI-powered nutritionists often take the form of websites or apps, leveraging machine learning to help users improve eating habits, tailor meal plans, and create recipes that align with personal preferences and health objectives.
With just a simple user input, AI meal planners can generate recipes that not only cater to dietary restrictions but also optimize the use of available ingredients. These tools also track caloric intake and monitor dietary goals. Moreover, AI nutritionists can analyze users' current health data and goals to create customized nutrition guidance and educational resources.2 They can even help users with specific health problems such as chronic diseases to manage their conditions.
Although the integration of AI into nutrition and health promotion offers numerous advantages, it also presents certain pitfalls. Limitations in data modeling can introduce functional biases and raise significant privacy concerns. Many AI tools currently lack the ability to forge complex interactions with users. Even with the ‘perfect’ AI nutritionist, human behavioral factors crucially influence adherence to guidance and plans, commonly leading to complete withdrawal from the tools.
This article delves into both the positive impacts and potential drawbacks of AI nutritionists. We also discuss strategies for incorporating behavioral perspectives into AI tools to effectively promote a balanced and healthy diet, aligning technological advancements with human behaviors and needs.
Advantages of AI in nutrition and health promotion
Easy access to low-cost, highly efficient solutions
Nutrition management has traditionally been a self-directed endeavor, often hindered by busy schedules that leave little room for individuals to seek professional guidance or consistently track their dietary habits. However, the emergence of AI nutritionists overcomes these challenges by increasing people’s accessibility to comprehensive nutrition knowledge and providing healthy eating solutions that are both sustainable and adaptable to individual schedules.
In contrast to traditional nutrition planning, which typically requires multiple in-person consultations that can be both costly and time-consuming, AI nutritionists streamline this process. They automate the collection and analysis of personal health data, significantly boosting efficiency and reducing costs by providing individualized nutritional guidance that is affordable and timely. Equipped with machine learning capabilities, they can further adapt diet plans and meal recommendations to accommodate individual dietary sensitivities, allergies, or health conditions.
References
- An, R. and X. Wang, Artificial Intelligence Applications to Public Health Nutrition. Nutrients, 2023. 15(19).
- Hamideh, D., et al., Your digital nutritionist. Lancet, 2019. 393(10166): p. 19.
- Oh, Y.J., et al., A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss. International Journal of Behavioral Nutrition and Physical Activity, 2021. 18(1): p. 160.
- Boland, M. and J. Bronlund, eNutrition - The next dimension for eHealth? Trends in Food Science & Technology, 2019. 91: p. 634-639.
- Karimian, G., E. Petelos, and S.M.A.A. Evers, The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI and Ethics, 2022. 2(4): p. 539-551.
- Russo, S. and S. Bonassi, Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology. Nutrients, 2022. 14(9).
- Konstantakopoulos, F.S., E.I. Georga, and D.I. Fotiadis, A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems. IEEE Rev Biomed Eng, 2024. 17: p. 136-152.
- Vandeputte, J., et al., Principles and Validations of an Artificial Intelligence-Based Recommender System Suggesting Acceptable Food Changes. J Nutr, 2023. 153(2): p. 598-604.
- Dias, S.B., et al., Users' Perspective on the AI-Based Smartphone PROTEIN App for Personalized Nutrition and Healthy Living: A Modified Technology Acceptance Model (mTAM) Approach. Front Nutr, 2022. 9: p. 898031.
- Chen, P.J. and M. Antonelli, Conceptual Models of Food Choice: Influential Factors Related to Foods, Individual Differences, and Society. Foods, 2020. 9(12).
- Rafacz, S.D., Healthy Eating: Approaching the Selection, Preparation, and Consumption of Healthy Food as Choice Behavior. Perspect Behav Sci, 2019. 42(3): p. 647-674.
- Ensaff, H., A nudge in the right direction: the role of food choice architecture in changing populations' diets. Proc Nutr Soc, 2021. 80(2): p. 195-206.
- Bauer, J.M. and L.A. Reisch, Behavioural Insights and (Un)healthy Dietary Choices: a Review of Current Evidence. Journal of Consumer Policy, 2019. 42(1): p. 3-45.
About the Author
Yuzhen (Valerie) Guo
Yuzhen is an applied behavioral scientist in healthcare. She leverages scientific methods and behavioral techniques to foster positive behavioral changes in health-related programs. In her work, she focuses on how behavioral sciences in communication and visual representation influence human decision-making. Yuzhen holds a Master's in Behavioral and Decision Sciences from the University of Pennsylvania, and a BA in Psychology and Organizational Sciences from The George Washington University. In her free time, Yuzhen enjoys expanding her knowledge of art history and exploring national parks.
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