Big data and AI: Potential and challenges for digital transformation in toxicology

Toxicology has evolved from an observational science to predictive science, driven by advances in computational methods and large-scale data generation. Advances in computing power and the rapid accumulation of toxicological big data have opened new opportunities to modernize chemical risk assessment through artificial intelligence (AI). This study explores the current status of toxicity databases and key methodologies of AI such as machine learning, deep learning, and large language models. The study further examines representative case studies, which leverage AI-based toxicity prediction models in chemical prioritization and others. Despite the advancements, critical challenges remain, including the limited availability of high-quality, homogeneous datasets and the black-box nature of AI models, which hinder regulatory acceptance. To address these issues, this study emphasizes the need for explainable AI and the integration of the adverse outcome pathway framework to enhance model interpretability. By outlining future research directions and advocating for transparent, reproducible AI models, this study contributes to advancing regulatory science, chemical safety assessment, and the broader adoption of AI as new approach methodologies (NAMs) for next generation risk assessment (NGRA).