The generative revolution: AI foundation models in geospatial health—applications, challenges and future research
Abstract In an era of rapid technological advancements, generative artificial intelligence and foundation models are reshaping industries and offering new advanced solutions in a wide range of scientific areas, particularly in public and environmental health. However, foundation models have previous...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | International Journal of Health Geographics |
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| Online Access: | https://doi.org/10.1186/s12942-025-00391-0 |
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| author | Bernd Resch Polychronis Kolokoussis David Hanny Maria Antonia Brovelli Maged N. Kamel Boulos |
| author_facet | Bernd Resch Polychronis Kolokoussis David Hanny Maria Antonia Brovelli Maged N. Kamel Boulos |
| author_sort | Bernd Resch |
| collection | DOAJ |
| description | Abstract In an era of rapid technological advancements, generative artificial intelligence and foundation models are reshaping industries and offering new advanced solutions in a wide range of scientific areas, particularly in public and environmental health. However, foundation models have previously mostly focused on understanding and generating text, while geospatial features, interrelations, flows and correlations have been neglected. Thus, this paper outlines the importance of research into Geospatial Foundation Models, which have the potential to revolutionise digital health surveillance and public health. We examine the latest advances, opportunities, challenges, and ethical considerations of geospatial foundation models for research and applications in digital health. We focus on the specific challenges of integrating geospatial context with foundation models and lay out the future potential for multimodal geospatial foundation models for a variety of research avenues in digital health surveillance and health assessment. |
| format | Article |
| id | doaj-art-ed227ef39d7e4f949d787f6eee29f1f6 |
| institution | OA Journals |
| issn | 1476-072X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | International Journal of Health Geographics |
| spelling | doaj-art-ed227ef39d7e4f949d787f6eee29f1f62025-08-20T01:56:09ZengBMCInternational Journal of Health Geographics1476-072X2025-04-0124111510.1186/s12942-025-00391-0The generative revolution: AI foundation models in geospatial health—applications, challenges and future researchBernd Resch0Polychronis Kolokoussis1David Hanny2Maria Antonia Brovelli3Maged N. Kamel Boulos4IT:U Interdisciplinary Transformation UniversitySchool of Rural, Surveying & Geoinformatics Engineering, National Technical University of AthensIT:U Interdisciplinary Transformation UniversityDepartment of Civil and Environmental Engineering, Politecnico Di MilanoSchool of Medicine, University of LisbonAbstract In an era of rapid technological advancements, generative artificial intelligence and foundation models are reshaping industries and offering new advanced solutions in a wide range of scientific areas, particularly in public and environmental health. However, foundation models have previously mostly focused on understanding and generating text, while geospatial features, interrelations, flows and correlations have been neglected. Thus, this paper outlines the importance of research into Geospatial Foundation Models, which have the potential to revolutionise digital health surveillance and public health. We examine the latest advances, opportunities, challenges, and ethical considerations of geospatial foundation models for research and applications in digital health. We focus on the specific challenges of integrating geospatial context with foundation models and lay out the future potential for multimodal geospatial foundation models for a variety of research avenues in digital health surveillance and health assessment.https://doi.org/10.1186/s12942-025-00391-0Generative AILarge language modelsAI agentsGeospatial healthHealth surveillanceAI-powered public health |
| spellingShingle | Bernd Resch Polychronis Kolokoussis David Hanny Maria Antonia Brovelli Maged N. Kamel Boulos The generative revolution: AI foundation models in geospatial health—applications, challenges and future research International Journal of Health Geographics Generative AI Large language models AI agents Geospatial health Health surveillance AI-powered public health |
| title | The generative revolution: AI foundation models in geospatial health—applications, challenges and future research |
| title_full | The generative revolution: AI foundation models in geospatial health—applications, challenges and future research |
| title_fullStr | The generative revolution: AI foundation models in geospatial health—applications, challenges and future research |
| title_full_unstemmed | The generative revolution: AI foundation models in geospatial health—applications, challenges and future research |
| title_short | The generative revolution: AI foundation models in geospatial health—applications, challenges and future research |
| title_sort | generative revolution ai foundation models in geospatial health applications challenges and future research |
| topic | Generative AI Large language models AI agents Geospatial health Health surveillance AI-powered public health |
| url | https://doi.org/10.1186/s12942-025-00391-0 |
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