Artificial intelligence as the new frontier in chemical risk assessment

Thomas Hartung

doi:10.3389/frai.2023.1269932

Artificial intelligence as the new frontier in chemical risk assessment

Front Artif Intell. 2023 Oct 17:6:1269932. doi: 10.3389/frai.2023.1269932. eCollection 2023.

Author

Thomas Hartung^{1

2}

Affiliations

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.
² CAAT-Europe, University of Konstanz, Konstanz, Germany.

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'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.

Keywords: big data; computational toxicology; machine learning; regulatory toxicology; scientific revolution.

Grants and funding

The author declares financial support was received for the research, authorship, and/or publication of this article. This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 963845 (ONTOX).