Application of Artificial Intelligence to Advance Environmental Health Research: A Review

Artificial intelligence (AI) encompasses any computer algorithm that makes predictions, recommendations,or decisions on the basis of a defined set of objectives. the recent growth of AI ineffectiveness and popularity is due largely to a branch of statistical AI known as machine learning(ML). Every application of AI affects the climate, which means aligning AI with climate changestrategies involves not only facilitating beneficial applications of AI, but also shaping the space ofAI overall so that business-as-usual applications are more climate-aligned. The machine learningenabled pathogen detection device performance were evaluated with water samples taken fromdifferent sources. IoT with machine learning algorithm can be used in waste management systemto develop the smart city in effective manner. It is clear that AI and IoT have an increasingly importantand innovative role to play in providing more sustainable waste management, dealing withtoday’s wastes, and moving forward to a zero-waste future based on the circular economy. Thenext frontier of technological advancements is the use of facility “flight simulators,” or “digitaltwins,” that enable dynamic process simulations. In recent years, data-driven analytics such asmachine learning has become key tools for discovery in public health and environmental scienceand engineering research. Discovering materials and chemicals based on machine learning israpidly growing. The results obtained from the review of articles from ۲۰۱۸ to ۲۰۲۳ show thatartificial intelligence algorithms are widely used in health, environment and sustainable development.Commonly used algorithms in environmental research are: linear regression, logisticsregression, decision tree, support vector machine, and random forest algorithm. The challengeis how to collect valid data. Data set biases occur when the training data are not representativeof the planned use case and can arise when training data are inadvertently contaminated with thedesired outcome information or when the training data are missing relevant examples. We shouldnot over trust or overestimate machine learning tools. Although challenges lie ahead, there arestill many opportunities (balance model fidelity and interpretability; data sharing; data collectionfrom trusted sources; applications of ml models and Educational).In this review, I introduce AIfor environmentalists and describe opportunities and threats of artificial intelligence and machinelearning algorithm in the environmental health contexts.