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’s ability to handle big data and provide probabilistic outputs facilitates probabilistic risk assessment.

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