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Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review

Year: 2025
Type: review
Abstract: PURPOSE OF REVIEW: Geospatial analysis is an essential tool for research on the role of environmental exposures and health, and critical for understanding impacts of environmental risk factors on diseases with long latency (e.g. cardiovascular disease, dementia, cancers) as well as upstream behaviors including sleep, physical activity, and cognition. There is emerging interest in leveraging machine learning and artificial intelligence (AI) for environmental epidemiology research. In this review, we provide an accessible overview of recent advances. RECENT FINDINGS: There have been two major recent shifts in geospatial data types and analytic methods. First, novel methods for statistical prediction, combining geospatial analysis with machine learning and artificial intelligence (GeoAI), allow for scalable geospatial exposure assessment within large population health databases (e.g. cohorts, administrative claims). Second, the widespread adoption of smartphones and wearables with global positioning systems and other sensors has allowed for passive data collection from people, and when combined with geographic information systems, enables exposure assessment at finer spatial scales and temporal resolution than ever before. Illustrative examples include refining models for predicting outdoor air pollution exposure, characterizing populations susceptible to water pollution, and use of deep learning to classify Street View image-derived measures of greenspace. While these tools and approaches may facilitate more rapid, higher quality objective exposure measures, they pose challenges with respect to participant privacy, representativeness of collected data, and curation of high quality validation sets for training of GeoAI algorithms. GeoAI approaches are beginning to be used for environmental exposure assessment and behavioral outcome ascertainment with higher spatial and temporal precision than before. Epidemiologists should continue to apply critical assessment of measurement accuracy and design validity when incorporating these new tools into their work.
DOI: 10.1007/s40572-025-00497-4
Authors: Hari S. Iyer, Seigi Karasaki, Li Yi, Yulin Hswen, Peter James, Trang VoPham
Institutions: Rutgers Cancer Institute, University of Washington, Fred Hutch Cancer Center, Harvard University, Harvard Pilgrim Health Care, University of California, San Francisco, University of California, Davis
Language: English
Open Access status: hybrid
Estimated APC paid: $3,990
Funders: National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute, NIH Office of the Director, National Cancer Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Environmental Health Sciences
Awards: G1823106566, G1849237129, G3198702309, G3370632243, G3979804608, G4050367616, G44434424, Improving causal inference in Alzheimer's Disease prevention research on modifiable risk factors: the Triangulation of Innovative Methods to EndAD (TIME-AD) project, G8949028958, G9000052929, Exposure to Per- and Polyfluoroalkyl Substances and Prostate Cancer: Opportunities for Prevention and Early Detection, Rutgers Center for Environmental Exposure and Disease (CEED), G979940031
OpenAlex ID: W4414547178
FWCI: 3.228
Cites: 202
Cited by: 20
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