EU Science Hub

Artificial Intelligence could lead to better decisions on chemicals

Artificial Intelligence for Chemical Risk Assessment
Artificial Intelligence for Chemical Risk Assessment
Jan 07 2020

The huge potential of Artificial Intelligence to improve regulatory decision making on chemicals is discussed in a recent journal article, co-authored by JRC staff.

Artifical Intelligence (AI) promises not only to improve the scientific and technical aspects of the chemical evaluation process, but also the social dimension of regulatory decision making.

As the basis for managing the risks of chemical exposure, the Chemical Risk Assessment (CRA) process can impact a substantial part of the economy, the health of hundreds of millions of people, and the condition of the environment.

However, the number of properly assessed chemicals falls short of societal needs due to a lack of experts for evaluation, interference of third party interests, and the sheer volume of potentially relevant information on the chemicals from disparate sources.

To explore ways in which computational methods may help overcome this discrepancy between the number of chemical risk assessments required on the one hand and the current pace of the CRA process on the other, the JRC organised a workshop on Artificial Intelligence for Chemical Risk Assessment.

The workshop identified a number of areas where AI could potentially increase the number and quality of regulatory risk management decisions based on CRA. Although interconnected, these areas were organised under two main themes: the scientific-technical process and social aspects of the decision making process.

Scientific and technical aspects include, for example, the use of “big data” for discovering biological knowledge and informing chemical safety assessments. The state-of-the-art in using big data in toxicology is reviewed extensively in a book with significant contributions from JRC scientists.

Social aspects of decision making include, for example, identifying chemicals of concern, finding experts and facilitating collaboration.

It is expected that further exploration of the topics covered in the workshop could eventually increase the efficiency and effectiveness of the CRA process. However, the only way to fully realise the applications of AI for CRA is to promote global and cross-disciplinary collaboration.

Recommended reading

Artificial Intelligence for chemical risk assessment Computational Toxicology

Big Data in Predictive Toxicology