From:  The role of artificial intelligence (AI) in foodborne disease prevention and management—a mini literature review

 Summary of AI applications in food safety and foodborne outbreak prevention.

Applications of AIOpportunitiesConsiderations
1) Predictive models for food safety and security
Monitoring environmental factors (weather, water, soil, temperature); hazard prediction (biological, chemical, physical); early warning systems; forecasting contamination risksAnticipate and prevent risks; enhance food chain monitoring; support sustainable food security; enable climate change adaptation strategiesRequire high-quality historical data; complex modeling; dependent on data sharing and standardization
2) AI in the food supply chain
Digitalization of production data; tracing, monitoring, inspection; supervised and unsupervised learning for anomaly detection; outbreak source identificationFaster and more accurate outbreak detection; real-time risk prediction; improved supply chain transparencyRisks of AI hallucinations; reliance on robust data infrastructure; interpretability of models
3) Public health surveillance
Linking syndromic surveillance to causative agents; anomaly detection via image recognition (hygiene, handwashing); prediction of outbreak risks and spreadEarly detection and intervention; prevent large-scale exposures; support rapid public health responseUnder-reporting and misreporting; potential misclassification by AI; need for human oversight
4) Laboratory investigations
Spectra-based analysis; hyperspectral imaging; bacterial strain differentiation (antibiotic resistance, virulence, host specificity); modeling bacterial growthRapid pathogen detection (hours vs. days); automation of labor-intensive tasks; improved accuracy; adaptable across food typesWorkforce readiness; need for interdisciplinary expertise; high upfront costs
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