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Abstract

This review explores the avenues for the application of Artificial Intelligence (AI) techniques in Glycemic Index (GI) related research. The necessity of sophisticated technologies to investigate various GI‐related studies in food analytics has been established in recent years. AI technologies have emerged as promising approaches to address these challenges. We identified six major AI technologies applied in GI research: Machine Learning, Reinforcement Learning, Deep Learning, Image Processing, Natural Language Processing, and Explainable AI. Some of our findings include: (a) There have been significant improvements in GI-related studies using AI technologies over the past decade. (b) Machine learning algorithms were widely used (c) Many researchers used custom datasets, with the predominance of research originating from North American countries. (d) Identification of limitations and future directions for GI‐related studies employing AI technologies. By embracing AI technologies, the field of food analytics is poised for substantial advancements in understanding and managing glycemic responses. Unlike existing reviews that mainly discuss nutritional or clinical aspects of the glycemic index, this study systematically examines the integration of AI and machine learning technologies in GI-related research. It highlights computational breakthroughs, methodological trends, and future directions for intelligent glycemic analysis.

Keywords

Artificial intelligence Glycemic index Machine learning Deep learning Food analytics

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Wanigasingha, N., Harshini, H., Ariyaratne, M., Fernando, T., Dikwatta, U. ., & Samarasinghe, U. . (2025). GI meets AI: Glycemic index in the age of AI, computational breakthroughs. Future Technology, 5(1), 93–126. Retrieved from https://fupubco.com/futech/article/view/526
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