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Nutrition Informatics
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Artificial intelligence technology started to be applied more broadly and find new uses in the fields of food science and nutrition research in the late 2010s. A new generation of cutting-edge artificial intelligence-based technologies and equipment is currently constantly infiltrating and integrating into the field of food nutrition, driving the global food nutrition industry to develop rapidly in the direction of personalisation, precision, and intelligence. This is due to the ongoing scientific and technological revolution as well as the in-depth evolution of industrial transformation (Zhu & Wang, 2023) .
The future will see a greater and more comprehensive use of artificial intelligence in the field of food nutrition due to ongoing technological advancements and the expansion of application scenarios. It is anticipated that more individualised nutrition recommendations and health management services will be achieved by incorporating more contextual data and creating an even better data system(Zhu & Wang, 2023) .
Kindly share your views on the implications of Artificial Intelligence in Food and Nutrition.
Reference:
1. Zhu, J., & Wang, G. (2023). Artificial Intelligence Technology for Food Nutrition. Nutrients, 15(21), 4562–4562. //doi.org/10.3390/nu15214562
Posted : April 15, 2024 9:37 pm
Artificial intelligence (AI) has revolutionized the field of food and nutrition, providing cutting-edge approaches for tailored dietary and health management. The incorporation of AI technologies like machine learning, neural networks, and natural language processing has propelled nutritional science forward.These advancements has given to the development of intelligent systems capable of analyzing complex dietary data, predicting individual nutritional needs, and determining the best food selections for health and wellness (1).
AI technologies have proven to be instrumental in supporting research within agriculture, food science, and nutrition sectors. The European Food Safety Authority’s report suggests that by 2030, food safety regulations research will likely call for enhanced collaboration with the broader society and a greater influx of societal data for risk assessment using AI, particularly employing AI and machine learning for instantaneous through real-time analysis of big data (3).
A 2024 study review highlighted AI’s transformative impact on nutritional science, especially in tasks such as dietary evaluation and predicting the toxicity of food ingredients. AI’s application in food toxicology has advanced significantly. While traditional food safety evaluations often involve animal testing, there is a growing call for alternatives that minimize or eliminate the need for such methods. Numerous studies have formulated quantitative structure-activity relationship models using deep neural networks, which draw from databases detailing chemical structures and toxicities. The effectiveness and safety of substances are influenced not only by their chemical and physical characteristics but also by individual genetic factors related to absorption, distribution, metabolism, and excretion. An ideal AI model for food toxicology in the future would account for these individual differences in dietary component processing (2).
References:
1. Armand, T., Kintoh Allen Nfor, Kim, J.-I., & Kim, H.-C. (2024). Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients, 16(7), 1073–1073. //doi.org/10.3390/nu16071073
2. Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Yoshifumi Miyawaki, Ashida, K., Jun Iimura, Tsuda, T., Hiroto Bushita, Kazuichi Tomonobu, Ohta, S., Chung, H., Yusuke Omae, Yamamoto, T., Morinaga, M., & Ochi, H. (2022). Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews, 80(12), 2288–2300. //doi.org/10.1093/nutrit/nuac033
3. Stef Bronzwaer, Kass, G., Robinson, T., Tarazona, J., Verhagen, H., Didier Verloo, Domagoj Vrbos, & Hugas, M. Food Safety Regulatory Research Needs 2030. EFSA Journal, 17(7). //doi.org/10.2903/j.efsa.2019.e170622
Posted : April 24, 2024 12:33 pm