Opportunities and shortcomings of AI for spatial epidemiology and health disparities research on aging and the life course.

Journal: Health & place
PMID: 39047648

Abstract

Established spatial and life course methods have helped epidemiologists and health and medical geographers study the impact of individual and area-level determinants on health disparities. While these methods are effective, the emergence of Geospatial Artificial Intelligence (GeoAI) offers new opportunities to leverage complex and multi-scalar data in spatial aging and life course research. The objective of this perspective is three-fold: (1) to review established methods in aging, life course, and spatial epidemiology research; (2) to highlight some of the opportunities offered by GeoAI for enhancing research on health disparities across life course and aging research; (3) to discuss the shortcomings of using GeoAI methods in aging and life course studies.

Authors

  • Hoda S Abdel Magid
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Dornsife Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA. Electronic address: hmagid@usc.edu.
  • Michael R Desjardins
    Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, USA.
  • Yingjie Hu
    GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.

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