Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment

Machine Learning (ML) and Artificial Intelligence (AI) approaches have potential to make better-informed decisions in chemical hazard identification while reducing animal testing. Their application in the context of New Approach Methodologies (NAMs) for Hazard Identification in Chemicals Risk Assessment (CRA) is challenging due to the limited knowledge, lack of experience, and uncertainty related to the use of these approaches. Therefore, to facilitate ML and AI approaches' potential acceptance