Affiliation:
1. Department of Food Science, Cornell University, Ithaca, New York, USA;
2. Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, New York, USA
Abstract
With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning models, natural language processing, and computer vision to improve food safety. Possible AI applications are broad and include, but are not limited to, ( a) food safety risk prediction and monitoring as well as food safety optimization throughout the supply chain, ( b) improved public health systems (e.g., by providing early warning of outbreaks and source attribution), and ( c) detection, identification, and characterization of foodborne pathogens. However, AI technologies in food safety lag behind in commercial development because of obstacles such as limited data sharing and limited collaborative research and development efforts. Future actions should be directed toward applying data privacy protection methods, improving data standardization, and developing a collaborative ecosystem to drive innovations in AI applications to food safety.
Cited by
27 articles.
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