TY - JOUR
KW - artificial intelligence (AI)
KW - Bias audit
KW - Causal modeling
KW - chemical risk assessment
KW - Digital Twins
KW - Ethical toxicology
KW - Explainable AI (xAI)
KW - Federated learning
KW - human relevance
KW - New Approach Methodologies (NAM)
KW - regulatory science
KW - Responsible AI
KW - TREAT principles
KW - Toxicology
KW - e-Validation
AU - Thomas Luechtefeld
AU - Thomas Hartung
AB - The 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.
BT - Current Environmental Health Reports
DA - 2025-12-05
DO - 10.1007/s40572-025-00514-6
IS - 1
LA - en
N2 - The 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.
PY - 2025
EP - 51
ST - Navigating the AI Frontier in Toxicology
T2 - Current Environmental Health Reports
TI - Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation
UR - https://doi.org/10.1007/s40572-025-00514-6
VL - 12
Y2 - 2025-12-05
SN - 2196-5412
ER -