Smart Digital & Predictive Systems: AI, Automation, and Multi-Omics for Proactive Food Safety and Quality Management

Recent advances in artificial intelligence (AI) have positioned the field as a transformative technological domain, with applications across many areas. AI encompasses methods such as machine learning and deep learning, among others. These approaches enable systems to perform tasks that are typically associated with human intelligence, such as learning, reasoning, perception, and decision-making.Despite the increasingly stringent food safety regulatory framework and the implementation of preventive systems such as HACCP, as well as more sophisticated risk-based management approaches, food safety and quality remain a major challenge for both governments and industry. The food sector is now transitioning towards Industry 4.0, adopting a digitally driven approach to production, automation, traceability, and logistics, built on several cutting-edge technologies.AI is emerging as a game-changer for ensuring food safety and quality in a highly complex supply chain. Although subject to legal and operational constraints, international and national health authorities have also begun to embrace AI to enhance risk assessment and strengthen decision-making. These are critical components of food policy and are often influenced by multidimensional factors. The application of AI-based technologies in the food sector has grown in recent years, from computer vision technologies to sensing technologies, omics, predictive microbiology, data fusion, and other data-intensive approaches. However, there is still a gap in consolidated knowledge regarding the application of AI in the food safety and quality context and the extent to which AI can generate a meaningful impact on public health, including through real-world validation, more effective food policies and control measures, risk anticipation, and rapid response to food alerts and crises.The goal of this Research Topic is to provide a comprehensive overview of the current use of AI-based technologies in the field of food safety and quality, including descriptions of their applications and limitations, methodological approaches, and practical implications.We welcome submissions addressing, but not limited to the following themes:- Artificial intelligence in food safety risk assessment and decision support- AI-driven monitoring and prediction of food safety and shelf life- Artificial intelligence across the digital food supply chain (e.g., logistics, traceability, data fusion)- AI integration with omics, big data, and food system surveillance- AI-enhanced preventive systems and Industry 4.0 in the food sector- Governance, regulation, and explainable AI in food safety and quality- Public health impact and societal implications of AI in food systems- AI-based solutions to support household hygiene practices and consumer information- AI in logistics optimization and cold chain managementSubmissions may include original research articles, reviews, perspectives, case studies, and methods papers addressing any of these themes or closely related topics. Topic editors: Fernando Perez Rodriguez, Qingli Dong Source: https://www.frontiersin.org/research-topics/75894/smart-digital-predictive-systems-ai-automation-and-multi-omics-for-proactive-food-safety-and-quality-management