AI’s Intelligence for Improving Food Safety: Only as Strong as the Data that Feeds It

Abstract Purpose of Review AI-based systems show great promise in advancing food safety control and surveillance systems, while they have important limitations that are often misunderstood by stakeholders. This review explores the fundamental components of AI through food safety applications, highlighting both its capabilities and constraints. Recent Findings AI has been effectively applied to outbreaks detection, allergen or spoilage issues, fraud, supply chain monitoring, traceability, quality control, shelf-life prediction, and risk mitigation. These applications integrate three key elements—sensing, reasoning, and actuating— within a structured five-step implementation cycle. Summary While AI has the potential to transform food safety, its effectiveness depends on timely access to robust, comprehensive, and unbiased data to ensure accuracy, reliability, and meaningful insights. Moving forward, it is essential to emphasize the human-in-the-loop approach. AI can support decision-making by providing actionable insights and improving efficiency, but humans must remain at the center of critical operations.