Public articles linked to the same research event.
Archives of toxicology This review, based on a structured literature search of PubMed/MEDLINE, Web of Science and Scopus (2000-2026) supplemented by guidance documents from OECD, FDA, EMA, EFSA and EPA, traces the development of AI in toxicology from rule-based expert systems to deep learning, large language model and multimodal architectures, reports that graph neural networks and multi-task deep learning have shown competitive performance in selected benchmark studies of drug-induced liver injury, hERG cardiotoxicity and Ames mutagenicity, and concludes that inconsistent external validation, limited generalisation of endpoint-specific models and hallucination in large language models leave unresolved regulatory risks, so AI models augment but cannot yet replace experimental toxicology.
This review, based on a structured literature search of PubMed/MEDLINE, Web of Science and Scopus (2000-2026) supplemented by guidance documents from OECD, FDA, EMA, EFSA and EPA, traces the development of AI in toxicology from rule-based expert systems to deep learning, large language model and multimodal architectures, reports that graph neural networks and multi-task deep learning have shown competitive performance in selected benchmark studies of drug-induced liver injury, hERG cardiotoxicity and Ames mutagenicity, and concludes that inconsistent external validation, limited generalisation of endpoint-specific models and hallucination in large language models leave unresolved regulatory risks, so AI models augment but cannot yet replace experimental toxicology.
This review, based on a structured literature search of PubMed/MEDLINE, Web of Science and Scopus (2000-2026) supplemented by guidance documents from OECD, FDA, EMA, EFSA and EPA, traces the development of AI in toxicology from rule-based expert systems to deep learning, large language model and multimodal architectures, reports that graph neural networks and multi-task deep learning have shown competitive performance in selected benchmark studies of drug-induced liver injury, hERG cardiotoxicity and Ames mutagenicity, and concludes that inconsistent external validation, limited generalisation of endpoint-specific models and hallucination in large language models leave unresolved regulatory risks, so AI models augment but cannot yet replace experimental toxicology.
This review, based on a structured literature search of PubMed/MEDLINE, Web of Science and Scopus (2000-2026) supplemented by guidance documents from OECD, FDA, EMA, EFSA and EPA, traces the development of AI in toxicology from rule-based expert systems to deep learning, large language model and multimodal architectures, reports that graph neural networks and multi-task deep learning have shown competitive performance in selected benchmark studies of drug-induced liver injury, hERG cardiotoxicity and Ames mutagenicity, and concludes that inconsistent external validation, limited generalisation of endpoint-specific models and hallucination in large language models leave unresolved regulatory risks, so AI models augment but cannot yet replace experimental toxicology.