Artificial intelligence in food safety and nutrition practices: opportunities and risks

Artificial intelligence (AI) is transforming food safety and nutrition practices by offering 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 facilitates personalized diet recommendations, automated dietary tracking, and virtual nutrition coaching through data integration across genomics, microbiome, and behavioral inputs. Despite these promising applications, AI introduces notable risks, including algorithmic hallucinations, biased training data, opaque decision-making processes, and ethical concerns regarding data privacy and consent. Furthermore, the lack of regulatory frameworks and unequal access to AI tools may exacerbate existing health disparities. This narrative review synthesizes the current developments in AI-based food and nutrition applications; explores emerging challenges; and highlights ethical, technical, and policy considerations. This paper also presents a roadmap for the responsible integration of AI into food systems, emphasizing transparency, equity, interdisciplinary collaboration, and global governance. AI holds great promise to enhance safety and nutrition at a global scale, but its success depends on how thoughtfully and ethically it is designed, deployed, and evaluated. This review aims to guide researchers, policymakers, and practitioners in aligning technological innovation with public health priorities.