Artificial intelligence (AI) is rapidly reshaping food safety management, offering new capabilities in data analysis, predictive modeling, and risk-based decision-making. A recently released technical publication, jointly developed by the Food and Agriculture Organization of the United Nations (FAO) and Wageningen Food Safety Research, provides the first global synthesis of AI applications across food safety domains, from laboratory testing and inspection to border control and regulatory efficiency.

Drawing from 141 scientific papers and real-world case studies, including examples from low- and middle-income countries, the report identifies three core areas of AI deployment: scientific advice, inspection and border control, and operational activities of food safety competent authorities. Use cases range from pathogen detection and import sampling prioritization to the use of language models for regulatory data processing.

More specifically, across the three core areas, the report identifies how AI is being applied to enhance food safety management and regulatory oversight:

• Scientific advice: AI is predominantly applied to support the generation of scientific advice, particularly by enhancing laboratory testing efficiency and cost-effectiveness. Additionally, research using AI has extended to understanding the causes of food contamination and foodborne diseases, thereby informing preventive measures.
• Inspection and border control: Predictive models are helping authorities target monitoring efforts based on environmental and historical data, while AI-driven tools are improving food authenticity verification and contaminant detection at borders.
• Operational activities of authorities: Real-time analytics and text mining from sources like social media and recall reports are enabling faster responses to emerging threats, thereby streamlining regulatory activities and reducing reliance on traditional lab testing.

At the same time, despite growing enthusiasm, the report underscores persistent challenges: data scarcity, capacity constraints, and the need for robust governance. Experts emphasize that AI is not a goal in itself but a tool to enhance public health protection, sustainability, and resilience in agri-food systems. Responsible adoption hinges on high-quality, interoperable data, ethical oversight, and cross-sector collaboration.

Key recommendations for food safety authorities include:

• Strengthening AI governance frameworks that prioritize transparency, accountability, and human rights
• Building AI literacy and capacity through training in data science and risk communication
• Improving data systems via partnerships and FAIR data principles
• Encouraging multi-stakeholder collaboration across public, private, and academic sectors
• Adopting systems thinking to integrate AI across the food value chain.

The full technical report, titled, Artificial Intelligence for Food Safety: A Literature Synthesis, Real-World Applications, and Regulatory Frameworks, can be accessed here.