Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures

Artificial intelligence (AI) in environmental health science is revolutionizing data analysis and problem-solving approaches. These technologies facilitate the prediction of environmental exposures and disease outcomes and enable the identification of causal relationships for subsequent hypothesis testing. AI techniques improve pollution research through the analysis of satellite imagery and the modeling of pollutant dispersion, while AI advances chemical safety evaluations in toxicology by examining extensive datasets. AI is instrumental in addressing pressing environmental challenges, including remediation of polluted sites and ensuring equitable healthcare applications to mitigate biases. The expanding availability of large-scale environmental, geospatial, and health outcome databases offers unprecedented opportunities for innovative applications. Their predictive capabilities are essential in disaster management, enabling real-time analysis and optimizing resource deployment amid climate-related crises. AI-driven approaches play a critical role in carbon capture and waste management efforts aimed at reducing environmental impact. Furthermore, AI can elucidate complex relationships between the exposome-defined as the totality of exposures throughout an individual's life-and health outcomes, facilitating preventative strategies. This review examines the capabilities and limitations of AI in environmental health and safety, providing insights into its judicious and effective use for environmental management and healthcare.