Food safety systems are undergoing a profound and urgent transformation, shifting from traditional end-product inspection models toward integrated, preventive, and predictive approaches supported by digital, genomic and data-driven technologies. Conventional frameworks face increasing limitations in the context of globalized supply chains, climate variability, emerging hazards, and growing sustainability demands. This structured narrative review critically examines the technological and governance trends driving the transition toward digital and predictive food safety systems, with particular emphasis on their implications for sustainability. Key enabling technologies (including artificial intelligence (AI), whole-genome sequencing (WGS), Internet of Things (IoT)-based monitoring, blockchain-enabled traceability, and predictive analytics) are analyzed in terms of their capacity to enhance early hazard detection, real-time surveillance, and risk anticipation across the food supply chain. Beyond a descriptive overview, this review integrates technological, regulatory, and governance dimensions to identify convergence points, implementation barriers, and sustainability trade-offs, with particular attention to small and medium-sized enterprises and low- and middle-income countries. Furthermore, a four-level Digital Maturity Framework is proposed to conceptualize progressive stages of technological integration, providing a structured pathway for the evolution from reactive to predictive food safety systems. While digital and predictive approaches offer significant potential to reduce food losses, improve transparency, and strengthen evidence-based decision-making, their effective implementation remains constrained by infrastructure gaps, data governance challenges, regulatory fragmentation, and unequal access to digital capabilities. Achieving resilient and sustainability-oriented food safety systems will therefore require coordinated innovation, regulatory harmonization, and inclusive digital transformation strategies.
Zazueta-Vega et al. (Fri,) studied this question.
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