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<p>AI-driven methods have been applied across mycotoxin research in detection, prediction, and control, offering high laboratory accuracy ( &gt; 90%), robust forecasting (75–99%), and emerging mitigation strategies (80–86%). Integrating AI with multi-omics data, mixture toxicity modeling, and standardized protocols promises to enhance food safety, streamline regulatory decision-making, and reduce animal testing.</p>

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Leveraging artificial intelligence for mycotoxin management in food systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Francesca Caloni,
  • Thomas Hartung

摘要

AI-driven methods have been applied across mycotoxin research in detection, prediction, and control, offering high laboratory accuracy ( > 90%), robust forecasting (75–99%), and emerging mitigation strategies (80–86%). Integrating AI with multi-omics data, mixture toxicity modeling, and standardized protocols promises to enhance food safety, streamline regulatory decision-making, and reduce animal testing.