01945nas a2200469   4500000000100000000000100001008004100002260001500043653000700058653001900065653001400084653003100098653001500129653001000144100002000154700002000174700002200194700002200216700001900238700002200257700001600279700001900295700002000314700002000334700001800354700002400372700001700396700002100413700002300434700001300457700002700470700001400497700002500511700001700536700001600553700001800569700001900587245013500606856004700741520067300788022001401461       2026                            d  c2026-02-1710aAI10aExplainable AI10aFAIR data10aHazard and risk assessment10aToxicology10aTrust1 aTimothy W. Gant1 aAlistair Boxall1 aDaniel Burgwinkel1 aMaryam Zare Jeddi1 aIvo Djidrovski1 aSteffi Friedrichs1 aBarry Hardy1 aThomas Hartung1 aDaniela Holland1 aAndreas Karwath1 aAnne Kienhuis1 aNicole Kleinstreuer1 aZhoumeng Lin1 aEmma L. Marczylo1 aAntonino Marvuglia1 aHua Qian1 aBennard van Ravenzwaay1 aPaul Rees1 aHaralambos Sarimveis1 aTewes Tralau1 aLucy Wilmot1 aAdam Zalewski1 aDavid Rouquié00aBuilding trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop  uhttps://doi.org/10.1007/s00204-025-04286-83 aArtificial Intelligence (AI) is increasingly influencing chemical risk assessment, enabling faster, more comprehensive, and potentially more ethical assessments. The application of AI in chemical risk assessment refers to both generative and predictive algorithms encompassing machine learning, to analyse complex chemical, biological, and environmental data and provide insights into adverse effect potential for humans and ecosystems. AI systems support the prediction of chemical hazards, exposure levels, and adverse effects by learning from experimental results, mechanistic models, and regulatory datasets, thereby enhancing the efficiency of safety evaluations.  a1432-0738