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Artificial Intelligence for chemical risk assessment.

Clemens Wittwehr1, Paul Blomstedt2, John Paul Gosling3

  • 1European Commission, Joint Research Centre (JRC), Ispra, Italy.

Computational Toxicology (Amsterdam, Netherlands)
|March 7, 2020
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) can enhance chemical risk assessment (CRA) by addressing expert shortages and information overload. AI applications can improve the quality and quantity of regulatory decisions for managing chemical exposure risks.

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Area of Science:

  • Environmental Science
  • Toxicology
  • Computational Chemistry

Background:

  • Chemical Risk Assessment (CRA) is crucial for managing chemical exposure, impacting economy, public health, and environment.
  • Current CRA faces limitations due to expert scarcity, third-party interference, and vast, disparate data.
  • A significant gap exists between the need for chemical risk assessments and the actual number and quality of assessments conducted.

Purpose of the Study:

  • To explore how computational methods, specifically Artificial Intelligence (AI), can overcome limitations in CRA.
  • To identify areas where AI can improve the efficiency and effectiveness of chemical risk management.
  • To discuss the integration of AI into both the scientific-technical and social/decision-making aspects of CRA.

Main Methods:

  • Organized a workshop on Artificial Intelligence for Chemical Risk Assessment (AI4CRA) by the European Commission's Joint Research Centre.
  • Identified and categorized potential AI applications within the CRA process.
  • Discussed AI's role in scientific-technical and social/decision-making aspects of CRA.

Main Results:

  • AI offers potential solutions for increasing the number and quality of CRA evaluations.
  • Identified key AI application areas: process simulation, evaluation support, problem identification, collaboration, expert finding, evidence gathering, systematic review, knowledge discovery, and cognitive modeling.
  • AI can address challenges related to expert shortages and information management in CRA.

Conclusions:

  • AI has the potential to significantly enhance regulatory risk management decisions based on CRA.
  • Integrating AI can help bridge the gap between the demand for and supply of adequate chemical risk assessments.
  • Further development and implementation of AI are recommended for improving chemical safety and environmental protection.