01497nas a2200205   4500000000100000000000100001008004100002260000900043653001300052653002900065653002100094653002600115653002600141100001900167245007600186300001200262490000600274520099700280022001401277       2023                            d  c202310abig data10aComputational toxicology10aMachine Learning10aRegulatory toxicology10ascientific revolution1 aThomas Hartung00aArtificial intelligence as the new frontier in chemical risk assessment  a12699320 v63 aThe 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'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.  a2624-8212