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Enhancing Environmental and Human Health Management Through the Integration of Advanced Revitalization Technologies Utilizing Artificial Intelligence

Summary

This paper describes how combining artificial intelligence with environmental monitoring can help us better understand how pollution harms our health. The authors propose a seven-step system that collects data on pollution levels in air, water, and soil alongside health information from communities. By using AI to analyze these massive datasets together, scientists and doctors can more quickly identify which pollutants are causing specific health problems and design better treatments for affected people and environments.

Background

Environmental pollution from anthropogenic activities poses significant threats to both ecosystem health and human well-being. Traditional approaches to environmental management and human health are often inadequate for addressing complex pollution challenges. The integration of advanced remediation technologies with artificial intelligence offers a promising framework for improving environmental restoration and health outcomes.

Objective

This review proposes a comprehensive AI-integrated model for systematically collecting, analyzing, and correlating environmental monitoring data with epidemiological health data to better understand pollution impacts on human health. The model aims to enhance decision-making for environmental remediation and personalized therapeutic interventions.

Results

The model demonstrates how AI can process large heterogeneous datasets to identify correlations between specific pollutants and physiological effects. Examples include methylmercury contamination and neurotoxic effects, with applications of bioremediation, phytoremediation, and nanobioremediation technologies supported by AI analysis.

Conclusion

The proposed AI-integrated model provides a systematic framework for connecting environmental pollution monitoring with human health outcomes, enabling more rapid and precise understanding of pollutant effects. Implementation requires careful data preparation, validation, and development of universal approaches for specific pollutants, overcoming current limitations in data heterogeneity and model reproducibility.
  • Published in:Toxics,
  • Study Type:Review,
  • Source: PMID: 39771062, DOI: 10.3390/toxics12120847
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