A number of research articles have been published that showcase real-world case studies of how machine learning is employed for the rapid detection of pathogens, preventing contamination incidents,¹⁶ or how the synergy between AI and blockchain technologies functions for enhancing traceability and transparency throughout the food supply chain.¹⁷ These studies provide great examples showing that the integration of these AI technologies ensures their accountability in assisting users to make quick and correct responses to both management and technical food safety issues.

From these successful AI application case studies,^{12–15,18,19} it can also be surmised that the successful application of AI technologies to FSIS work will depend on the successful development and deployment of specific AI application approaches and methods that meet the needs of specific operation procedures and steps, types of products, inspection requirements, monitoring and control systems, and management purposes.^14,15,18 Keeping in mind that the purposes of AI application to food safety and inspection are to solve the most challenging and complex food safety and quality problems across various domains, it is essential to correctly determine what AI application approaches and methods should be chosen.