Evaluation of acute, subacute, and subchronic toxicity of a hepatoprotective herbal formulation

Bemidinezhad, Abolfazl; Zojaji, Seyyed Abbas; Taraz Jamshidi, Shirin; Mohammadi, Mostafa; Alavi, Mohaddeseh Sadat; Ghorbani, Ahmad

doi:10.1016/j.toxrep.2023.11.002

Toxicology Reports

2023

DOI: 10.1016/j.toxrep.2023.11.002

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Evaluation of acute, subacute, and subchronic toxicity of a hepatoprotective herbal formulation

Abolfazl Bemidinezhad,

Seyyed Abbas Zojaji,

Shirin Taraz Jamshidi

et al.

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2024

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AI in Predictive Toxicology

Yingngam

2024

Advances in Medical Technologies and Clinical Practice

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The field of toxicology is undergoing a significant transformation due to the integration of artificial intelligence (AI). In addition to traditional reliance on empirical studies and animal testing, AI-powered predictive toxicology is now used to predict the toxic effects of chemicals and drugs. This chapter examines the role of AI in enhancing the accuracy, efficiency, and breadth of toxicological assessments by bridging the gap between traditional approaches and advanced AI techniques. It explores various AI methodologies, such as machine learning, deep learning, and neural networks, focusing on their application in toxicity prediction. Furthermore, this chapter investigates the integration of AI with toxicological databases and the development and empirical validation of predictive models. It also addresses various challenges associated with AI-powered toxicology, including data quality, model interpretability, and scalability. The chapter concludes that despite facing challenges, AI is a powerful tool in modern toxicological analysis.

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AI in Predictive Toxicology

Yingngam

2024

Advances in Medical Technologies and Clinical Practice

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Evaluation of acute, subacute, and subchronic toxicity of a hepatoprotective herbal formulation

Cited by 1 publication

References 18 publications

AI in Predictive Toxicology

AI in Predictive Toxicology

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