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Artificial Intelligence-Driven Environmental Toxicology: Predictive Toxicity Modelling, Forensic Pollution Analysis, and AI-Enabled Public Health Surveillance
Summary
This research review shows how artificial intelligence and machine learning can help scientists better predict how environmental pollutants might harm human health, replacing slower traditional testing methods. AI can analyze huge amounts of environmental data to identify pollution sources, predict toxic effects, and track public health threats in real-time. This technology could help protect communities by catching environmental health risks earlier and providing better evidence for legal cases against polluters.
Rapidly advancing technologies, including artificial intelligence, are needed to tackle environmental health issues. Traditional environmental toxicology has relied on experimental bioassays, epidemiological studies, and statistical models. However, the increasing volume and complexity of environmental data are not well served by existing frameworks. Artificial intelligence and machine learning provide new opportunities to improve environmental toxicology modelling and health surveillance. This article analyses the most promising artificial intelligence and machine learning techniques for modelling environmental toxicology, with a focus on forensic environmental studies and health risk assessment. The article reviews the state of the art in predictive toxicology modelling, quantitative structure-activity relationship models, deep learning, and largescale data frameworks. In addition, the article explores environmental learning models to identify sources and patterns in environmental data, including the integration of forensic data to support the legal use of environmental data. This study also addresses the use of artificial intelligence in environmental epidemiology, the application of smart technologies in epidemiology, and the assessment of population exposure to environmental toxins. The review discusses key issues such as data negligence, model explainability, legal acceptability, ethical issues, regulatory impediments, and emerging research pathways, including hybrid mechanistic-AI models, federated learning, multi-omics, and global AI-driven monitoring. This review highlights the scope of AI-Environmental Toxicology for improving scientific accuracy, forensic accountability, and proactive data-driven public health advocacy.
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