AI-Driven Food Fraud Detection Systems: A Critical Systematic Review of the Detection–Prevention Gap – Protocol

Key Points

• This systematic review aims to evaluate how artificial intelligence can improve food fraud detection and identify gaps in prevention efforts.
• Follows PRISMA 2020 guidelines.
• Includes predefined eligibility criteria and a search strategy.
• Utilizes data extraction, risk-of-bias assessment, and statistical analyses.
• Expected to identify effective AI applications for food fraud detection.
• Aims to clarify the differences in detection in laboratory settings versus real-world scenarios.