Explainable Artificial Intelligence for Predictive Toxicology and Public Health Risk Assessment: A Data-Driven Framework for Early Detection and Decision Support

Public health systems continue to face increasing challenges from environmental and chemical exposures that contribute to disease burden and population level risks. Traditional toxicological assessment methods are often limited by high cost, long experimental timelines, and difficulties in translating laboratory findings into real world decision making. This study presents a data driven framework that integrates explainable artificial intelligence techniques into predictive toxicology for improved public health risk assessment. The proposed approach combines machine learning models with interpretable mechanisms to support early detection of toxicological risks while maintaining transparency in model predictions. Multi source datasets including environmental exposure records, clinical health data, and chemical toxicity profiles are utilized to develop and validate the framework. The study demonstrates how interpretable predictive models can enhance risk classification accuracy and support evidence based public health interventions. Findings suggest that integrating explainability into predictive systems improves trust, usability, and policy relevance in toxicological applications. The framework contributes to advancing computational toxicology and offers practical implications for health agencies, researchers, and decision makers seeking timely and reliable risk assessment tools.