POTENTIAL APPLICATIONTS OF ARTIFICIAL INTELLIGENCE IN NUTRITION SCIENCE

<ns0:p>Recently, there has been a wave-like surge in interest regarding the potential of artificial intelligence (AI) across nearly all fields of science and technology. Given this trend, it is important to note that existing machine learning algorithms are in a constant state of development and refinement. These algorithms are dependent on satellite technologies that enable high-quality collection of data necessary for further analysis. It is clear that current AI models possess not only advantages but also dynamically evolving limitations. The application possibilities of AI require further research, modernization, and assessment from a standpoint of practical value.In our study, we focused on the current possibilities of AI in the field of nutrition, specifically in the context of developing personalized dietary recommendations, optimizing health, and supporting scientific research. Recognizing the potential of AI to enhance the work of nutritionists and improve health outcomes, we aimed to distinguish the promising aspects of this technology from its current realities.The objective of this research was to analyze contemporary AI technologies and methods to assess their impact on the development of nutrition science, outline the advantages and limitations of these technologies, and explore prospects for further use. The research methods included a systematic review of scientific sources, an overview of modern AI technologies and models actively employed for nutrition personalization. Particular attention was given to the capabilities of computer vision, deep machine learning algorithms, and wearable devices in analyzing the nutritional value of food, monitoring dietary habits, and adapting dietary recommendations.The results indicate that AI can significantly enhance the quality of data collection and analysis regarding actual human nutrition, including by minimizing the “human factor.” Advantages also include its ability to increase the level of dietary personalization and to identify triggers of unhealthy eating behaviors, with recommendations for correction. In conjunction with wearable devices, this technology allows for dynamic diet adjustments. However, the technology remains dependent on advancements in wearable device development and requires standardized approaches and algorithms for its application.The practical value of this study lies in fostering a critical approach to the potential for implementing this technology in both the practical and scientific work of nutrition specialists.</ns0:p>