aceAdvances in AI's Future in Toxicology: Integrating Computational Prediction and Clinical Translation Through Explainable Artificial Intelligence
Artificial intelligence (AI) and machine learning are increasingly used in toxicological risk assessment to predict chemical toxicity, identify hazardous compounds, and support regulatory decision-making. However, the widespread adoption of these models is limited by their "black-box" nature, which reduces interpretability, transparency, and regulatory confidence. Explainable artificial intelligence (XAI) has emerged as a promising approach to address these challenges by revealing how input features, including c…
AI and machine learning models are being applied to predict chemical toxicity and flag hazardous compounds to inform regulatory decisions, with XAI methods like SHAP and LIME improving interpretability.
Current AI toxicity models are limited by black-box opacity that lowers transparency and regulatory confidence, with most explainable AI applications still stuck at computational or preclinical stage.
Most XAI work in toxicology has not yet translated to clinical or regulatory practice and requires further standardization and human data integration.
Evidence
- Peer-reviewedInternational Journal of Toxicology2026-08-12
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Truvace Impact Record TRV-2026-0757, v1: “Advances in AI's Future in Toxicology: Integrating Computational Prediction and Clinical Translation Through Explainable Artificial Intelligence.” Truvace, 2026-08-14. /record/TRV-2026-0757 (accessed at citation time). sha256 5efdca1bb67913d8…
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