Smart technology for public health: reshaping the future of food safety
Bibliographic record
Abstract
The food consumed globally, a fundamental element of life, is under threat from the rising complexities of modern supply chains and global distribution networks. As these networks expand, so do the risks of contamination, quality degradation, and safety breaches, jeopardizing billions of lives and eroding trust in the global food supply. This paper explores how smart technologies—blockchain, artificial intelligence (AI), the Internet of Things (IoT), and digital twins—are reshaping food safety through transparency, real-time monitoring, and predictive risk management. Key case studies illustrate their implementation and impact, including blockchain's role in rapid traceability, AI's predictive risk assessment capabilities, IoT's support for continuous monitoring, and digital twins' predictive simulations to prevent hazards. These tools collectively promote sustainability, operational efficiency, and consumer trust. Yet, widespread adoption remains challenged by technical, financial, and regulatory barriers. This review also tackles the socio-economic implications of smart technologies in food safety, highlighting disparities in technology access, particularly in developing regions. A systematic literature search using databases such as Scopus and Web of Science were conducted to synthesize peer-reviewed studies, industry reports, and case examples over the last decade. By integrating technical advances with socio-economic insights, this work offers a holistic perspective on the smart tech transformation in food safety. Accordingly, it presents a call to action for policymakers, industry stakeholders, and researchers to build a resilient, inclusive, and technology-enabled global food safety system—one that ensures every meal is safe, high-quality, and reflective of the power of innovation and cooperation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".