Artificial intelligence: A key fulcrum for addressing complex environmental health issues.

Journal: Environment international
PMID: 40121790

Abstract

Environmental health (EH) is a complex and interdisciplinary field dedicated to the examination of environmental behaviours, toxicological effects, health risks, and strategies for mitigating harmful environmental factors. Traditional EH research investigates correlations between risk factors and health outcomes through control variables, but this route is difficult to address complex EH issue. Artificial intelligence (AI) technology not only has accelerated the innovation of the scientific research paradigm but also has become an important tool for solving complex EH problems. However, the in-depth and comprehensive implementation of AI in the field of EH still faces many barriers, such as model generalizability, data privacy protection, algorithm transparency, and regulatory and ethical issues. This review focuses on the compound exposures of EH and explores the potential, challenges, and development directions of AI in four key phases of EH research: (1) data collection, fusion, and management, (2) hazard identification and screening, (3) risk modeling and assessment and (4) EH management. It is not difficult to see that in the future, artificial intelligence technology will inevitably carry out multidimensional simulation of complex exposure factors through multi-mode data fusion, so as to achieve accurate identification of environmental health risks, and eventually become an efficient tool for global environmental health management. This review will help researchers re-examine this strategy and provide a reference for AI to solve complex exposure problems.

Authors

  • Lei Huang
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Qiannan Duan
    State Key Laboratory of Pollution Control and Resource Reuse, Jiangsu Key Laboratory of Vehicle Emissions Control, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Yuxin Liu
    School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Division of Life Sciences and Medicine, Hefei, 230026, Anhui, China.
  • Yangyang Wu
    College of Animal Science and Technology Sichuan Agricultural University Ya'an Sichuan China.
  • Zenghui Li
    State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Zhao Guo
    School of Power and Mechanical Engineering, Wuhan University, 430072, Wuhan, China.
  • Mingliang Liu
  • Xiaowei Lu
    State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Peng Wang
    Neuroengineering Laboratory, School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China.
  • Fan Liu
    Hunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, Huaihua, China.
  • Futian Ren
    State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Jiaming Wang
    Institute of Biophysics, Chinese Academy of Science, Beijing 100101, China.
  • Yujia Huang
    State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China.
  • Beizhan Yan
    Lamont-Doherty Earth Observatory, Columbia University, New York, USA.
  • Marianthi-Anna Kioumourtzoglou
  • Patrick L Kinney
    Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA. Electronic address: pkinney@bu.edu.

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