Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop

Artificial Intelligence (AI) is increasingly influencing chemical risk assessment, enabling faster, more comprehensive, and potentially more ethical assessments. The application of AI in chemical risk assessment refers to both generative and predictive algorithms encompassing machine learning, to analyse complex chemical, biological, and environmental data and provide insights into adverse effect potential for humans and ecosystems. AI systems support the prediction of chemical hazards, exposure levels, and adverse effects by learning from experimental results, mechanistic models, and regulatory datasets, thereby enhancing the efficiency of safety evaluations.In October 2024, ECETOC held an international workshop, with experts from academia, industry, and regulatory bodies, to reflect upon the historical challenges in integrating multidimensional omics technologies into chemical regulation and explore the current capabilities and future potential of AI in toxicology and regulatory science. Discussions emphasised that implementation of Findable, Accessible, Interoperable, and Reusable (FAIR) data principles is not just a best practice but rather a prerequisite for building transparent, reliable, and unbiased AI systems. The reliability of AI in producing scientifically valid and socially responsible outcomes depends fundamentally on the availability of FAIR data. However, ensuring trustworthiness also requires robust governance frameworks that go beyond data and human oversight. Critical enablers of responsible AI in chemical risk assessment are rigorous governance, explainability, fit-for-purpose applications, and human oversight. ECETOC supports the development of flexible and iterative frameworks advancing development, validation, transparency, accountability, and trust in AI applications in chemicals regulation.

Authors

Institutions

Publication Details

Journal
Archives of Toxicology
Published
2026-02-17
DOI
https://doi.org/10.1007/s00204-025-04286-8
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop

Anne S. Kienhuis, Maryam Zare Jeddi, Barry Hardy, Antonino Marvuglia et al.
Archives of Toxicology
Computational Drug Discovery Methods
article

Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop

Anne S. Kienhuis, Maryam Zare Jeddi, Barry Hardy, Antonino Marvuglia, Haralambos Sarimveis, Daniela Holland, Emma L. Marczylo, Paul Rees, Timothy W. Gant, Tewes Tralau, LUCY E. WILMOT, Steffi Friedrichs, Ivo Djidrovski, Hua Qian, David Rouquié, Zhoumeng Lin, Daniel Burgwinkel, Andreas Karwath, Bennard van Ravenzwaay, Nicole Kleinstreuer, Adam Zalewski, Alistair Boxall
article en

Abstract

Artificial Intelligence (AI) is increasingly influencing chemical risk assessment, enabling faster, more comprehensive, and potentially more ethical assessments. The application of AI in chemical risk assessment refers to both generative and predictive algorithms encompassing machine learning, to analyse complex chemical, biological, and environmental data and provide insights into adverse effect potential for humans and ecosystems. AI systems support the prediction of chemical hazards, exposure levels, and adverse effects by learning from experimental results, mechanistic models, and regulatory datasets, thereby enhancing the efficiency of safety evaluations.In October 2024, ECETOC held an international workshop, with experts from academia, industry, and regulatory bodies, to reflect upon the historical challenges in integrating multidimensional omics technologies into chemical regulation and explore the current capabilities and future potential of AI in toxicology and regulatory science. Discussions emphasised that implementation of Findable, Accessible, Interoperable, and Reusable (FAIR) data principles is not just a best practice but rather a prerequisite for building transparent, reliable, and unbiased AI systems. The reliability of AI in producing scientifically valid and socially responsible outcomes depends fundamentally on the availability of FAIR data. However, ensuring trustworthiness also requires robust governance frameworks that go beyond data and human oversight. Critical enablers of responsible AI in chemical risk assessment are rigorous governance, explainability, fit-for-purpose applications, and human oversight. ECETOC supports the development of flexible and iterative frameworks advancing development, validation, transparency, accountability, and trust in AI applications in chemicals regulation.

Archives of Toxicology
Johns Hopkins University (US), University of Applied Sciences Utrecht (NL), National Technical University of Athens (GR), Environment Agency (GB), University of Konstanz (DE), Utrecht University (NL), University of Florida Health (US), Federal Institute for Risk Assessment (DE), University College Birmingham (GB), University of Florida (US), Public Health England (GB), Swansea University (GB), European Centre for Ecotoxicology and Toxicology of Chemicals (BE), National Institute for Public Health and the Environment (NL), Luxembourg Institute of Science and Technology (LU), Division of Program Coordination Planning and Strategic Initiatives (US), Edelweiss Connect (Switzerland) (CH), Bayer (France) (FR), Imperial College London (GB), University of York (GB), University of Florida Health Science Center (US), Bayer (Germany) (DE), Shell (Netherlands) (NL), University of Birmingham (GB), Wageningen University & Research (NL), ExxonMobil (United States) (US)
European Commission, U.S. Food and Drug Administration, Staatssekretariat für Bildung, Forschung und Innovation
Openalex Percentile: Top 53%
Computational Drug Discovery Methods
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.