A global perspective on artificial intelligence applications and barriers in food safety: A systematic review (2018–2025)

Food safety remains a global priority, and the use of artificial intelligence (AI) to mitigate risks across the food supply chain has expanded rapidly. However, much of the existing literature treats AI adoption as a uniform process and pays limited attention to differences in macroeconomic contexts. This study provides a comparative analysis of AI adoption in food safety between high-income countries (HICs) and low- and middle-income countries (LMICs), focusing on application areas, key actors, and implementation challenges. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixty-five empirical studies published between 2018 and 2025 were identified from Scopus (Elsevier, Amsterdam, Netherlands), Web of Science (Clarivate Analytics, Philadelphia, USA), and Google Scholar (Google, Mountain View, California, USA). The findings show clear structural differences in AI utilisation. In HICs, AI strengthens mature food safety systems, supports regulatory compliance, and facilitates predictive risk management. In LMICs, AI is mainly applied to basic screening, operational risk control, and traceability, but adoption is constrained by limited data availability, weak digital infrastructure, and insufficient technical capacity. Overall, AI adoption reflects institutional maturity and systemic capacity rather than technological capability alone, highlighting the need for context-specific implementation strategies.