摘要   隨著環(huán)境和健康研究數(shù)據(jù)共享及可用性的不斷提升，涉及環(huán)境與人體健康的數(shù)據(jù)集數(shù)量急劇增加。然而，這些環(huán)境健康大型數(shù)據(jù)集多樣且復雜，傳統(tǒng)的流行病學和環(huán)境健康模型難以有效分析，因此催生了一個環(huán)境健康研究的新手段。人工智能（AI）技術在環(huán)境健康領域的應用正迅速發(fā)展，為新污染物篩選和毒性預測、生物監(jiān)測、風險評估和健康保護提供了新穎且強大的工具。其中，先進的機器學習（ML）算法能夠揭示人類難以察覺的規(guī)律，在生物標志物識別、疾病預防和環(huán)境工程優(yōu)化等方面表現(xiàn)出重要潛力，為環(huán)境健康研究和技術創(chuàng)新提供新的思路和突破口。然而，ML技術在環(huán)境健康領域的應用仍面臨數(shù)據(jù)質量、模型解釋性以及跨學科合作等挑戰(zhàn)。本文將綜述ML技術在環(huán)境健康領域的最新應用進展，探討其優(yōu)勢、挑戰(zhàn)以及未來的發(fā)展方向，以期為環(huán)境保護和公共健康領域的研究和實踐提供有價值的參考。

Abstract   As the data sharing and availability in environmental and health research continue to improve, the number of large datasets for environmental and human health has increased dramatically. However, these large environmental health datasets are diverse and complex, and traditional epidemiological and environmental health models are difficult to effectively analyze, leading to the development of a new approach to environmental health research. The application of artificial intelligence (AI) technology in environmental health is rapidly developing, providing novel and powerful tools for new pollutant screening and toxicity prediction, biomonitoring, risk assessment, and health protection. Among them, advanced machine learning (ML) algorithms can reveal laws that are difficult for humans to detect, showing important potential in biomarker identification, disease prevention, and environmental engineering optimization. This can provide new ideas and breakthroughs for environmental health research and technological innovation. However, the application of ML technology in the field of environmental health still faces challenges such as data quality, model interpretability, and interdisciplinary cooperation. This paper will review the latest progress in the application of ML technology in the field of environmental health, discuss its advantages, challenges, and future development directions, with the aim of providing valuable references for research and practice in the fields of environmental protection and public health.

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