SynthesisMycotoxin research2025
The use of artificial intelligence to improve mycotoxin management: a review.
Synthesis in Mycotoxin research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis 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
11 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Predictive Artificial Intelligence Models for Mycotoxin Surveillance and Mitigation in Poultry Production Systems.Toxins · 2026Review
- Aflatoxin decontamination in tree nuts: a scoping review of physical, chemical, and biological methods.Mycotoxin research · 2026Article
- Identification and Detection of AflatoxigenicToxins · 2026Review
- Review
- Projected mycotoxin contamination in European wheat under future climate and socioeconomic scenarios.NPJ science of food · 2026Article
- Climate-stressed crop mycobiomes as pre-harvest sentinels of human mycotoxin exposure: an AI-enabled One Health framework linking dysbiosis, exposomics, and toxicopathology.Frontiers in systems biology · 2026Review
- Agroecological drivers of aflatoxin contamination in maize-based systems: a review.Frontiers in plant science · 2026Review
- Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial Contaminants.Foods (Basel, Switzerland) · 2025Review
- Silent Saboteurs: Decoding Mycotoxins-From Chemistry and Prevalence to Health Risks, Detection, Management and Emerging Frontiers.Journal of fungi (Basel, Switzerland) · 2025Review
- Processing and Real-Time Monitoring Strategies of Aflatoxin Reduction in Pistachios: Innovative Nonthermal Methods, Advanced Biosensing Platforms, and AI-Based Predictive Approaches.Foods (Basel, Switzerland) · 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
4 authors.
Funding
Abstract
The management of mycotoxin contamination in the supply chain is continuously evolving in response to growing knowledge about mycotoxins, shifting factors that influence mycotoxin occurrence, and ongoing technological developments. One of the technological developments is the potential for using artificial intelligence (AI) in mycotoxin management. AI can be used in various fields of mycotoxin management, including for predictive modelling of mycotoxins and for analytical detection and analyses. This review aimed to investigate the state-of-the-art of the use of AI for mycotoxin management. This review focuses on (1) predictive models for the presence of mycotoxins in commodities at both pre-harvest and post-harvest levels and (2) the detection of mycotoxins in samples by processing large datasets resulting from imaging data or chemical analyses of the sample. A systematic review was conducted, resulting in a total of 70 relevant references, including 15 references focusing on mycotoxin prediction models and 54 references focusing on mycotoxin detection, ranging from imaging to chemical analysis, and including relevant reviews. The AI applications and the most popular AI algorithms are presented. As shown by this review, AI is able to improve mycotoxin prediction models both at pre- and post-harvest levels and makes the emergence of non-invasive and fast detection methods such as imaging detection or electronic noses possible. A major challenge remains in the applicability and scalability of AI models to practical settings.
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.