Artificial intelligence for food safety. A literature synthesis, real-world applications and regulatory frameworks

Artificial Intelligence (AI) is increasingly applied in food safety management, offering new capabilities in data analysis, predictive modelling, and risk-based decision-making. A review of the literature identifies three primary areas of application: scientific advice, inspection and border control, and operational activities of food safety competent authorities. Five country examples with the real-world use cases illustrate diverse uses of AI tools, including pathogen detection, import sampling prioritization, and language models for regulatory data processing. Regulatory frameworks, as well as voluntary governance, addressing AI in the public sector are emerging worldwide. National and international initiatives often highlight the importance of data governance, transparency, ethical considerations, and human oversight. Challenges such as biased data, explainability, and data governance gaps appear across different contexts, along with potential risks from deploying AI systems prematurely. Access to high-quality, interoperable data and collaboration among stakeholders can support effective integration of AI technologies. AI readiness often depends on understanding specific problems to be addressed, current capacities, and the quality of available data. Human oversight and continuous evaluation contribute to maintaining trust in AI systems. Collaborative efforts involving academia, the private sector, and international organizations help build shared knowledge and resources for AI development in food safety. Overall, AI presents opportunities to enhance resilience, efficiency, and responsiveness in food safety systems. Careful consideration of governance, data management, and multi-stakeholder cooperation can shape AI’s contribution to achieving sustainable and equitable outcomes in agrifood systems.