ReviewFoods (Basel, Switzerland)2026
Generative and Foundation-Model Artificial Intelligence in Food Safety Analysis, Authenticity Identification, and Quality Control: A Systematic Review of Validation and Decision Readiness.
Review in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative and foundation-model artificial intelligence can strengthen food analysis, but computational performance does not establish decision readiness. This systematic review examines applications and validation requirements in food safety, authenticity, and quality control. A Scopus search on 13 June 2026 identified 438 records; 65 publications were retained, including 41 direct analytical reports and 24 supporting publications. Independent dual screening and coding supported thematic synthesis and descriptive evidence mapping. The direct core comprised 18 safety-related, 4 authenticity, 9 quality-related, and 10 adjacent-domain reports. Of these, 23 reported experimental or reference-method validation, 13 dataset/model evaluation, and 5 human, sensory, or expert assessment. None of the 41 direct reports documented prospective evaluation in a real industrial setting. This finding remained unchanged in narrower-scope analyses, although the balance among experimental, dataset/model, and human-assessment validation changed. The generated food-evidence transfer gap integrates established transfer problems with food-specific decision requirements. Five cumulative gates organize provenance, technical validity, food-science validity, human calibration, and deployment governance. The framework connects generative-AI evidence requirements to established analytical standards rather than replacing them. Decision readiness requires risk-proportionate evidence for a specified use, including applicable reference-method and regulatory requirements. These findings characterize the captured literature, not the complete industrial deployment landscape.
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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.