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

Autor: Abdel Magid HS; 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., Desjardins MR; 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., Hu Y; GeoAI Lab, Department of Geography, University at Buffalo, Buffalo, NY, USA; Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA.
Jazyk: angličtina
Zdroj: Health & place [Health Place] 2024 Sep; Vol. 89, pp. 103323. Date of Electronic Publication: 2024 Jul 23.
DOI: 10.1016/j.healthplace.2024.103323
Abstrakt: 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.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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