Opportunities and Risks Based on Food Safety and Nutrition Ai: A Mathemat ical Approach
Abstract:
Artificial intelligence (AI) is reshaping food safety and nutrition practices by delivering scalable, real-time, and personalized solutions. In food safety, AI enables predictive risk modeling, rapid contaminant detection, smart surveillance systems, and blockchain-based traceability. In nutrition, AI supports personalized diet rec ommendations, automated dietary tracking, and virtual nutrition coaching by integrating data from genomics, the microbiome, and behavioral inputs. Despite these advancements, AI introduces significant risks, including algorithmic hallucinations, biased training data, opaque decision-making, and ethical concerns related to data privacy and consent. Additionally, the absence of robust regulatory frameworks and unequal access to AI tools may widen existing health disparities. This narrative review synthesizes current developments in AI-based food and nutrition applications, examines emerging challenges, and highlights ethical, technical, and policy con siderations. It also proposes a roadmap for the responsible integration of AI into food systems, emphasizing transparency, equity, interdisciplinary collaboration, and global governance. While AI holds immense potential to enhance safety and nutrition worldwide, its success ultimately depends on how thoughtfully and ethically it is designed, implemented, and evaluated. This review aims to guide researchers, policymakers, and practitioners in aligning technological innovation with public health priorities.
