Sep 2026· Archives of Toxicology· Vol 100, pp. 4989 - 5007· 0 citations· 84 references
Medicine
TL;DR
This review critically evaluates the evidence base for AI-based toxicity prediction, assesses current challenges and considers the trajectory toward next-generation risk assessment, which requires adherence to the TREAT validation principles and sustained regulatory engagement.
This review discusses recent progress in ML, DL, and AI-based strategies for toxicity prediction, assesses their utility across major toxicity endpoints, and highlights the challenges and future research opportunities in predictive toxicology.
Anjali Mahavar, Atul Patel, Ashish Patel· Current Chemical Biology· 0 citations
Predictive artificial intelligence applications in pollutant hazard assessment, environmental monitoring, exposure modeling, multi-omics integration, and regulatory decision support are examined.
Ehsan Ghassemi Barghi, F. Motafeghi, Jafar Gholami Gharab et al.· Discover Artificial Intellig...· 0 citations
The significance of AI-based toxicogenomics in drug development, environmental contaminant screening, and regulatory toxicology, while addressing key challenges such as data heterogeneity, model interpretability, and regulatory acceptance is discussed.
Mohammed Monirul Islam, Sibghatullah Muhammad Ali Sangi, Girish Meravanige et al.· Frontiers in Genetics· 0 citations
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.
The increasing burden of persistent organic pollutants, per‐ and polyfluoroalkyl substances, heavy metals, and emerging contaminants represents a global threat to human, animal, and ecosystem health. Current management strategies remain fragmented and reactive, limiting the effectiveness of the One Health paradigm. Thi...
This review examines recent developments in AI-based TK parameterization with relevance to environmental exposure assessment and discusses current limitations and future perspectives, including model reliability, interpretability, data availability for environmental chemicals, and integrating AI-predicted TK parameters...
Xue Wu, Zhi-Cheng Zhang, Kun Mi et al.· Journal of Environmental Exp...· 0 citations
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