Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Artificial Intelligence for chemical risk assessment
AU - Wittwehr, Clemens
AU - Blomstedt, Paul
AU - Gosling, John Paul
AU - Peltola, Tomi
AU - Raffael, Barbara
AU - Richarz, Andrea-Nicole
AU - Sienkiewicz, Marta
AU - Whaley, Paul
AU - Worth, Andrew
AU - Whelan, Maurice
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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.In order 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 number and adequateness of assessments actually being conducted on the other, the European Commission's Joint Research Centre organised a workshop on Artificial Intelligence for Chemical Risk Assessment (AI4CRA).The workshop identified a number of areas where Artificial Intelligence could potentially increase the number and quality of regulatory risk management decisions based on CRA, involving process simulation, supporting evaluation, identifying problems, facilitating collaboration, finding experts, evidence gathering, systematic review, knowledge discovery, and building cognitive models. Although these are interconnected, they are organised and discussed under two main themes: scientific-technical process and social aspects and the decision making process.
AB - 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.In order 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 number and adequateness of assessments actually being conducted on the other, the European Commission's Joint Research Centre organised a workshop on Artificial Intelligence for Chemical Risk Assessment (AI4CRA).The workshop identified a number of areas where Artificial Intelligence could potentially increase the number and quality of regulatory risk management decisions based on CRA, involving process simulation, supporting evaluation, identifying problems, facilitating collaboration, finding experts, evidence gathering, systematic review, knowledge discovery, and building cognitive models. Although these are interconnected, they are organised and discussed under two main themes: scientific-technical process and social aspects and the decision making process.
U2 - 10.1016/j.comtox.2019.100114
DO - 10.1016/j.comtox.2019.100114
M3 - Journal article
VL - 13
JO - Computational Toxicology
JF - Computational Toxicology
M1 - 100114
T2 - Artificial Intelligence for Chemical Risk Assessment
Y2 - 4 April 2018 through 5 April 2018
ER -