01905nas a2200265   4500000000100000000000100001008004100002260000900043653003200052653001200084653002800096653001100124653002100135653003300156653001800189653002000207653003800227653002600265100001900291245012200310300001200432490000700444520117400451022001401625       2023                            d  c202310aAnimal Testing Alternatives10aAnimals10aArtificial intelligence10aHumans10aMachine Learning10aartificial intelligence (AI)10adeep learning10aneural networks10aNew Approach Methodologies (NAMs)10apredictive toxicology1 aThomas Hartung00aToxAIcology - The evolving role of artificial intelligence in advancing toxicology and modernizing regulatory science  a559-5700 v403 aToxicology has undergone a transformation from an observational science to a data-rich discipline ripe for artificial intelligence (AI) integration. The exponential growth in computing power coupled with accumulation of large toxicological datasets has created new opportunities to apply techniques like machine learning and especially deep learning to enhance chemical hazard assessment. This article provides an overview of key developments in AI-enabled toxicology, including early expert systems, statistical learning methods like quantitative structure-activity relationships (QSARs), recent advances with deep neural networks, and emerging trends. The promises and challenges of AI adoption for predictive toxicology, data analysis, risk assessment, and mechanistic research are discussed. Responsible development and application of interpretable and human-centered AI tools through multidisciplinary collaboration can accelerate evidence-based toxicology to better protect human health and the environment. However, AI is not a panacea and must be thoughtfully designed and utilized alongside ongoing efforts to improve primary evidence generation and appraisal.  a1868-8551