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|Title:||Artificial Intelligence for Chemical Risk Assessment|
|Authors:||WITTWEHR CLEMENS; BLOMSTEDT PAUL; GOSLING JOHN PAUL; PELTOLA TOMI; RAFFAEL BARBARA; RICHARZ ANDREA; SIENKIEWICZ MARTA; WHALEY PAUL; WORTH ANDREW; WHELAN MAURICE|
|Citation:||Computational Toxicology vol. 13 no. 100114 p. 1-7|
|Type:||Articles in periodicals and books|
|Abstract:||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 sufficient 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 AI 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.|
|JRC Directorate:||Health, Consumers and Reference Materials|
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