@article{6551,
  keywords = {artificial intelligence (AI), Bias audit, Causal modeling, chemical risk assessment, Digital Twins, Ethical toxicology, Explainable AI (xAI), Federated learning, human relevance, New Approach Methodologies (NAM), regulatory science, Responsible AI, TREAT principles, Toxicology, e-Validation},
  author = {Thomas Luechtefeld and Thomas Hartung},
  title = {Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation},
  abstract = {The integration of artificial intelligence (AI) into toxicology marks a profound paradigm shift in chemical safety science. No longer limited to automating traditional workflows, AI is redefining how we assess risk, interpret complex biological data, and inform regulatory decision-making. This article explores the convergence of AI and other new approach methodologies (NAMs), emphasizing key trends such as multimodal learning, causal inference, explainable AI (xAI), generative modeling, and federated learning.},
  year = {2025},
  journal = {Current Environmental Health Reports},
  volume = {12},
  pages = {51},
  month = {2025-12-05},
  issn = {2196-5412},
  url = {https://doi.org/10.1007/s40572-025-00514-6},
  doi = {10.1007/s40572-025-00514-6},
  language = {en},
}