@article{bibcite_2821,
  keywords = {big data, Computational toxicology, Machine Learning, Regulatory toxicology, scientific revolution},
  author = {Thomas Hartung},
  title = {Artificial intelligence as the new frontier in chemical risk assessment},
  abstract = {The rapid progress of AI impacts various areas of life, including toxicology, and promises a major role for AI in future risk assessments. Toxicology has shifted from a purely empirical science focused on observing chemical exposure outcomes to a data-rich field ripe for AI integration. AI methods are well-suited to handling and integrating large, diverse data volumes - a key challenge in modern toxicology. Additionally, AI enables Predictive Toxicology, as demonstrated by the automated read-across tool RASAR that achieved 87\% balanced accuracy across nine OECD tests and 190,000 chemicals, outperforming animal test reproducibility. AI{\textquoteright}s ability to handle big data and provide probabilistic outputs facilitates probabilistic risk assessment. Rather than just replicating human skills at larger scales, AI should be viewed as a transformative technology. Despite potential challenges, like model black-boxing and dataset biases, explainable AI (xAI) is emerging to address these issues.},
  year = {2023},
  journal = {Frontiers in Artificial Intelligence},
  volume = {6},
  pages = {1269932},
  month = {2023},
  issn = {2624-8212},
  doi = {10.3389/frai.2023.1269932},
  language = {eng},
}