ArticleFrontiers in nutrition2025
AI-driven transformation in food manufacturing: a pathway to sustainable efficiency and quality assurance.
Article in Frontiers in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Hybrid Intelligence for Fouling Prediction and Adaptive Clean-in-Place Optimization in Food Processing Heat Exchangers: A Systematic Review.Journal of food science · 2026Pooled it
- Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.Mycotoxin research · 2026Pooled it
- Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data.Sensors (Basel, Switzerland) · 2026Review
- Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction.Food chemistry: X · 2026Review
- Non-thermal processing of polyphenol- and carotenoid-rich green leafy vegetables for healthy aging: effects on antioxidant properties and bioavailability.Frontiers in nutrition · 2026Review
- Artificial intelligence-driven food and nutrition systems: from smart food production to personalized nutrition.Frontiers in nutrition · 2026Article
- Does the Digital Economy Promote Dietary Diversity Among Chinese Residents?Foods (Basel, Switzerland) · 2025Article
- Review
- Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions.Frontiers in nutrition · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to explore the transformative role of Artificial Intelligence (AI) in food manufacturing by optimizing production, reducing waste, and enhancing sustainability. This review follows a literature review approach, synthesizing findings from peer-reviewed studies published between 2019 and 2024. A structured methodology was employed, including database searches and inclusion/exclusion criteria to assess AI applications in food manufacturing. By leveraging predictive analytics, real-time monitoring, and computer vision, AI streamlines workflows, minimizes environmental footprints, and ensures product consistency. The study examines AI-driven solutions for waste reduction through data-driven modeling and circular economy practices, aligning the industry with global sustainability goals. Additionally, it identifies key barriers to AI adoption-including infrastructure limitations, ethical concerns, and economic constraints-and proposes strategies for overcoming them. The findings highlight the necessity of cross-sector collaboration among industry stakeholders, policymakers, and technology developers to fully harness AI's potential in building a resilient and sustainable food manufacturing ecosystem.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.