The Use of AI in Scientific Research on Pollution and Diseases
Modern artificial intelligence and machine learning methods are increasingly used in research related to environmental pollution and associated diseases. The application of big data analysis, neural networks, geographic information systems, and predictive models enables the identification of complex relationships between pollution factors and human health, as well as the assessment of long-term risks to ecosystems and populations.
This discussion focuses on the opportunities, limitations, and ethical aspects of using artificial intelligence in scientific research on soil, water, and air pollution, epidemiology of diseases, and the assessment of environmental and health impacts of military and technogenic activities.
Main Areas of Discussion
- application of machine learning and neural network models for the analysis of environmental pollution data
- utilization of artificial intelligence in epidemiological research and disease modeling
- analysis of large-scale datasets (Big Data) within the pollution–exposure–disease framework
- integration of artificial intelligence with geographic information systems (GIS) and Earth observation data
- prediction and assessment of environmental and public health risks using artificial intelligence–based algorithms
- limitations, reproducibility, robustness, and validation of artificial intelligence models
- ethical, legal, and regulatory considerations related to the use of artificial intelligence in scientific research