Assessing the reliability of AI-driven predictive models in food safety risk management

Sustaining food safety in a world that has undergone globalization with complex supply chains makes it very challenging. Conventional risk management methodologies are sometimes inadequate in addressing emerging risks, making the use of AI and predictive modeling invaluable tools in enhancing food safety evaluation and decision making. AI-based models have the prospects of identifying contamination risks at an early stage, enhancing the optimization of hazardous control measures, and increasing the effectiveness of compliance monitoring. Nevertheless, the reliability of their predictions is still questionable due to factors such as data integrity, model interpretability, and regulatory compliance that influence their applicability in practice. Despite AI's potential, challenges such as inconsistent data sources, varying regulatory standards, and adaptability across diverse food production environments limit its efficacy. Addressing these issues is crucial for AI-driven models to be fully integrated into food safety management systems. This paper provides a critical review of the current AI-based predictive models for food safety risk management synthesizing insights from existing literature, industry studies and regulatory reports. Through an evaluation of the strengths, drawbacks and limitations of these models, this research emphasizes the importance of developing standardized validation frameworks and improved strategies for integrating data across models. Overall, this research enriches the current discourse regarding the use of AI in food safety and offers key recommendations for improving model reliability and performance in protecting consumers and safeguarding public health Keywords: Artificial Intelligence (AI), Predictive Modeling, Food Safety Risk Management, Data-Driven Decision Making.