Developing artificial intelligence models to predict and manage air pollution-related diseases using genetic data

Air pollution remains one of the most pressing environmental threats to human health, contributing to a wide spectrum of diseases ranging from respiratory and cardiovascular conditions to neurological, oncological, and autoimmune disorders. While traditional epidemiological studies have established the harmful effects of pollutants, the complexity of disease mechanisms necessitates advanced approaches that integrate biological, genetic, and environmental dimensions. This review explores the emerging role of artificial intelligence (AI) in predicting and managing air pollution–related diseases, with particular emphasis on the contribution of genetic data. Genetic information, when processed through bioinformatics pipelines, provides critical insights into individual susceptibility, gene–environment interactions, and molecular pathways that mediate pollution-induced pathologies. The integration of such data into AI models enhances their predictive capacity, enabling early identification of vulnerable populations and supporting the transition toward personalized medicine. Various machine learning and deep learning frameworks, including neural networks and ensemble models, are highlighted for their ability to analyze large, heterogeneous datasets and uncover non-linear relationships that elude traditional statistical approaches. Despite its promise, this field faces several challenges, including the collection and validation of high-quality data, the ethical and privacy implications of genomic information, and the methodological hurdles of creating transparent, interpretable, and generalizable models. Nonetheless, successful studies demonstrate the potential of AI-driven systems to forecast disease risks, guide clinical interventions, and inform public health policies. By bridging computational intelligence with genetics and environmental health, AI offers a transformative framework for mitigating the global health burden of air pollution.

Keywords:

Artificial intelligence , Air Pollution–Related Diseases , Genetic Data , Machine Learning , Environmental Health