Artificial Intelligence in Nutrition: A Comprehensive Review of Applications and Challenges

Artificial intelligence has ingrained itself in almost all industries, including public health and nutrition, in this era of Industry 4.0, which refers to the automation and digitization of business. Considering the growing prevalence of diet-related diseases and the well-heightened demand for personalized dietary interventions, artificial intelligence certainly stands to provide innovative solutions to optimizing such nutritional strategies. Employing a hybrid methodology combining the PRISMA standards and the systematic literature review criteria from leading academic databases, this paper presents a systematic review of AI potential in terms of machine learning and deep learning techniques with remarkable findings in the field of AI in nutrition. A rigorous study selection and quality assessment process identified key AI applications in personalized nutrition, nutritional assessment, food recognition and predictive modelling for disease prevention, diagnosis and monitoring. AI-driven food recognition systems also enormously improve dietary tracking, enabling food logging through automatic image recognition, thereby greatly reducing human errors in assessing nutritional values. The paper covers challenges discussed in the literature of this field and future directions of research. Using the full potential of AI, the future of AI-driven nutrition can definitely improve public health and lead to efficiently, equally accessible and evidence-based dietary interventions.