From detection to prediction: rethinking foodborne pathogen intelligence in the age of AI
TL;DR
This perspective argues that the more consequential shift is genuine predictive capability: forecasting contamination before it manifests, enabling proactive intervention rather than reactive damage control.
Abstract
Artificial intelligence is reshaping foodborne pathogen surveillance from a retrospective, laboratory-bound model into a predictive, real-time intelligence layer embedded in food supply chains. While much enthusiasm focuses on AI accelerating detection, this perspective argues that the more consequential shift is genuine predictive capability: forecasting contamination before it manifests, enabling proactive intervention rather than reactive damage control. We examine three converging technological clusters and show how they collectively challenge the detection-centric paradigm of food-safety microbiology. Despite remarkable progress, industrial deployment faces persistent barriers: poor model generalizability across food matrices, scarcity of annotated data sets, lack of interpretability, and regulatory frameworks ill-suited to AI-based methods. Overcoming these obstacles requires advances in algorithmic robustness, edge computing, data standardization, explainable AI, and regulatory science. AI can fundamentally reconfigure food safety from detecting failures after they occur to preventing harm before it begins, a true paradigm shift toward a preventive food supply chain.