Potential applications of artificial intelligence with large datasets for predicting food biotoxicity

Abstract Food safety is a critical global concern, as toxic substances in food pose serious risks to public health. With the rise of novel food products such as cell-cultured, fermented, and genetically modified items, there is an urgent need for more efficient and accurate methods to assess food toxicity. Traditional testing approaches often lack the speed, scalability, and sensitivity needed to detect emerging toxicants. Omics-based technologies now offer comprehensive insights into biological responses, enabling the identification of subtle or unknown toxic effects. However, the complexity and scale of omics data present significant challenges for interpretation. To address this, artificial intelligence (AI) has emerged as a powerful tool to analyze large datasets and improve toxicity prediction. In this review, we summarize key categories of food toxicants, introduce omics technologies and publicly available databases, outline general AI modeling workflows, and highlight recent applications of AI in food safety. Together, AI with large amount of food-related data are shaping the future of food safety strategies.