@article{7106,
  keywords = {AI, Explainable AI, FAIR data, Hazard and risk assessment, Toxicology, Trust},
  author = {Timothy W. Gant and Alistair Boxall and Daniel Burgwinkel and Maryam Zare Jeddi and Ivo Djidrovski and Steffi Friedrichs and Barry Hardy and Thomas Hartung and Daniela Holland and Andreas Karwath and Anne Kienhuis and Nicole Kleinstreuer and Zhoumeng Lin and Emma L. Marczylo and Antonino Marvuglia and Hua Qian and Bennard van Ravenzwaay and Paul Rees and Haralambos Sarimveis and Tewes Tralau and Lucy Wilmot and Adam Zalewski and David Rouquié},
  title = {Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop},
  abstract = {Artificial Intelligence (AI) is increasingly influencing chemical risk assessment, enabling faster, more comprehensive, and potentially more ethical assessments. The application of AI in chemical risk assessment refers to both generative and predictive algorithms encompassing machine learning, to analyse complex chemical, biological, and environmental data and provide insights into adverse effect potential for humans and ecosystems. AI systems support the prediction of chemical hazards, exposure levels, and adverse effects by learning from experimental results, mechanistic models, and regulatory datasets, thereby enhancing the efficiency of safety evaluations.},
  year = {2026},
  journal = {Archives of Toxicology},
  month = {2026-02-17},
  issn = {1432-0738},
  url = {https://doi.org/10.1007/s00204-025-04286-8},
  doi = {10.1007/s00204-025-04286-8},
  language = {en},
}