Food safety – the transition to artificial intelligence (AI) modus operandi

Abstract

Background

The integration of artificial intelligence (AI) represents a revolutionary advancement in the global food safety paradigm, particularly in the transition from historically reactive measures to predictive and preventive methodologies. In the past, laws concerning food safety were created mainly to address emergencies and prevent both adulteration and obvious contamination. However, recent AI developments have made it possible to handle pathogen detection, assess risks and monitor the supply chain more quickly, accurately and efficiently.

Scope and approach

This critical review analyses significant historical milestones, from ancient practices through medieval regulations to transformative discoveries of the industrial era, and ultimately towards contemporary technological integration.

Key findings and conclusions

AI can indeed be a valuable tool in enhancing the efficiency of food safety regulations, and it is a natural progression in the historical transition toward increased acceptance of AI by public sector institutions. Convolutional neural networks, hyperspectral imaging, and blockchain-based traceability demonstrate how AI has enhanced food safety management by detecting and preventing issues early on. This review highlights the significant challenges that remain, including data availability, the opacity of algorithms (the “black box” problem), substantial implementation costs, and specialized skill requirements. We outline the progression from reactive, historically driven food safety regulations to proactive AI-powered predictive and preventive strategies, examining the associated strengths, limitations, opportunities, and threats. Lastly, the review provides policymakers, those in the food sector, and academics with the knowledge and guidance they need to adopt and effectively apply AI technologies to enhance food safety.