AI-driven food safety: transforming food inspection, traceability, and compliance in food industry and regulatory bodies: A mini review

Abstract

The increasing demand for high-quality, safe food products, coupled with global challenges such as foodborne illnesses, antimicrobial resistance, and the complexities of food production and supply chains, necessitates the adoption of advanced technologies in the food industry. This review explores the role of Artificial Intelligence (AI) in enhancing food safety and quality assurance systems in food regulatory bodies and industry. We examine various AI applications, including machine learning, computer vision, predictive analytics, and Natural Language Processing (NLP), which have the potential to improve food safety monitoring, contamination detection, and regulatory compliance. AI-driven automation and data analysis tools are transforming food safety practices by enhancing efficiency, accuracy, and real-time decision-making capabilities. The integration of AI with other technologies, such as the Internet of Things (IoT), also facilitates better traceability and proactive risk management in the food supply chain. However, despite these advancements, challenges such as data privacy, industry reluctance, and the lack of standardization must be addressed for wider adoption. The paper concludes by highlighting the need for interdisciplinary collaboration, improved data standards, and continued innovation to unlock the full potential of AI in food safety and quality systems.