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Accepted Articles of Congress

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  • Applications, challenges, and perspectives of artificial intelligence & neural networks in toxicology: Narrative review

  • elham saber,1,* Sara Karimi Zeverdegani ,2 Samira Barakat,3
    1. 2. PhD Student, Student Research Committee AND Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
    2. 1. Associate Professor, Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
    3. 3. PhD Student, Student Research Committee AND Department of Occupational Health and Safety Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran


  • Introduction: Artificial neural networks (ANN), as a significant subset of artificial intelligence, exhibit substantial potential within the domain of toxicology research. These networks possess the capability to anticipate elevated levels of substance toxicity. Furthermore, these networks are adept at discerning intricate chemical-biological interactions that are often beyond the reach of conventional toxicology investigations.
  • Methods: In this study, the databases utilized include ISI Web of Science, Scopus, PubMed Central (via PubMed), Science Direct, and Google Scholar. The article's search terms encompassed: "artificial intelligence," "artificial neural networks," and "toxicology."
  • Results: In light of the accelerating advancements in artificial intelligence and its associated fields, it is anticipated that artificial neural networks will assume a pivotal role in the classification of various toxins and the examination of their biochemical properties
  • Conclusion: In light of the accelerating advancements in artificial intelligence and its associated fields, it is anticipated that artificial neural networks will assume a pivotal role in the classification of various toxins and the examination of their biochemical properties. This research underscores the significance and contribution of artificial neural networks in the exploration of toxic substances.
  • Keywords: Artificial intelligence (AI), Artificial Neural Networks (ANN), toxicology

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