01463nas a2200349   4500000000100000000000100001008004100002260001500043653003300058653001500091653002000106653002900126653001800155653002300173653002500196653002300221653002000244653003700264653002300301653001900324653002100343653001500364653001700379100002300396700001900419245008000438856004700518300000700565490000700572520052000579022001401099       2025                            d  c2025-12-0510aartificial intelligence (AI)10aBias audit10aCausal modeling10achemical risk assessment10aDigital Twins10aEthical toxicology10aExplainable AI (xAI)10aFederated learning10ahuman relevance10aNew Approach Methodologies (NAM)10aregulatory science10aResponsible AI10aTREAT principles10aToxicology10ae-Validation1 aThomas Luechtefeld1 aThomas Hartung00aNavigating the AI Frontier in Toxicology: Trends, Trust, and Transformation  uhttps://doi.org/10.1007/s40572-025-00514-6  a510 v123 aThe integration of artificial intelligence (AI) into toxicology marks a profound paradigm shift in chemical safety science. No longer limited to automating traditional workflows, AI is redefining how we assess risk, interpret complex biological data, and inform regulatory decision-making. This article explores the convergence of AI and other new approach methodologies (NAMs), emphasizing key trends such as multimodal learning, causal inference, explainable AI (xAI), generative modeling, and federated learning.  a2196-5412