Artificial intelligence as the new frontier in chemical risk assessment

Frontiers in Artificial Intelligence

Artificial Intelligence

Thomas Hartung¹ 

[1] Center for Alternatives to Animal Testing (CAAT), Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States;CAAT-Europe, University of Konstanz, Konstanz, Germany;

关键词: computational toxicology;    machine learning;    big data;    regulatory toxicology;    scientific revolution;   

DOI  :  10.3389/frai.2023.1269932

 received in 2023-07-31, accepted in 2023-09-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

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. 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.

【 授权许可】

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