Artificial Intelligence and Machine Learning in Environmental Health Science: A Review of Emerging Applications

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are transforming environmental health science because they allow the deep analysis of multi-dimensional, raw measurements or signal-level information gathered across a variety of data sources coming reliable satellite imagery, IoT sensors, epidemiological databases, and genomic data. The paper reviews the potential of AI and ML to change environmental health as it applies in predicting air and water quality, forecasting and predicting vector-borne diseases, climate change impacts on health, and models of risk of toxicity of a chemical compound. Other critical issues that have been addressed in the study are data heterogeneity, model accuracy, scalability, algorithmic bias and ethical issues associated with data privacy and transparency. The obstacles towards the implementation of AI/ML solutions in low-resource environments are addressed with particular focus, and the dangers of the situation exacerbating health disparities are determined by data deficits and insufficient infrastructure. In sum, the review makes the conclusion that, on the one hand, AI and ML provide a liberating potential in environmental health research and policy, but, on the other hand, their benefits will be optimized when applied in collaboration with human expertise, ethical regulation, and inclusion in data collection practices to make sure of its equitable, responsible implementation.