The generative revolution: AI foundation models in geospatial health-applications, challenges and future research.

Bernd Resch, Polychronis Kolokoussis, David Hanny, Maria Antonia Brovelli, Maged N Kamel Boulos
Author Information
  1. Bernd Resch: IT:U Interdisciplinary Transformation University, 4040, Linz, Austria.
  2. Polychronis Kolokoussis: School of Rural, Surveying & Geoinformatics Engineering, National Technical University of Athens, 15780, Athens, Greece.
  3. David Hanny: IT:U Interdisciplinary Transformation University, 4040, Linz, Austria.
  4. Maria Antonia Brovelli: Department of Civil and Environmental Engineering, Politecnico Di Milano, 20133, Milan, Italy.
  5. Maged N Kamel Boulos: School of Medicine, University of Lisbon, 1649-028, Lisbon, Portugal. mnkboulos@ieee.org.

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.

Keywords

References

  1. Front Artif Intell. 2024 Nov 19;7:1430984 [PMID: 39628839]
  2. Brief Bioinform. 2022 Jan 17;23(1): [PMID: 34791014]
  3. J Crit Care. 2018 Jun;45:95-104 [PMID: 29413730]
  4. Health Informatics J. 2024 Jul-Sep;30(3):14604582241275844 [PMID: 39172555]
  5. Nat Med. 2020 Jun;26(6):900-908 [PMID: 32424212]
  6. Front Artif Intell. 2021 Jun 29;4:694875 [PMID: 34268489]
  7. Curr Epidemiol Rep. 2022;9(3):175-182 [PMID: 35789918]
  8. NPJ Digit Med. 2024 Jul 8;7(1):183 [PMID: 38977771]
  9. Front Public Health. 2023 Oct 26;11:1196397 [PMID: 37954052]
  10. Int J Health Geogr. 2019 May 2;18(1):7 [PMID: 31043176]
  11. Nat Med. 2023 Jul;29(7):1857-1866 [PMID: 37429922]
  12. Sci Adv. 2021 Mar 5;7(10): [PMID: 33674304]
  13. Sensors (Basel). 2019 Jan 18;19(2): [PMID: 30669293]
  14. IEEE Rev Biomed Eng. 2025;18:172-191 [PMID: 39531565]
  15. Sci Adv. 2023 Jan 18;9(3):eabq0199 [PMID: 36652520]
  16. Health Inf Sci Syst. 2022 Sep 14;10(1):28 [PMID: 36120113]
  17. JMIR Infodemiology. 2025 Jan 30;5:e58539 [PMID: 39883923]
  18. Radiology. 2020 Apr;295(1):4-15 [PMID: 32068507]
  19. NPJ Digit Med. 2023 Jul 29;6(1):135 [PMID: 37516790]
  20. Sci Total Environ. 2023 Mar 15;864:161135 [PMID: 36566867]
  21. Sci Rep. 2024 Aug 6;14(1):18227 [PMID: 39107395]
  22. Sci Rep. 2021 Mar 26;11(1):6955 [PMID: 33772039]
  23. Environ Res. 2022 Mar;204(Pt A):111970 [PMID: 34474031]
  24. Diagnostics (Basel). 2023 Jun 02;13(11): [PMID: 37296799]
  25. Nature. 2023 Aug;620(7972):172-180 [PMID: 37438534]
  26. Ger Med Sci. 2022 Mar 04;20:Doc01 [PMID: 35465641]
  27. Spat Spatiotemporal Epidemiol. 2020 Aug;34:100355 [PMID: 32807400]
  28. PLoS Med. 2006 Dec;3(12):e473 [PMID: 17147467]
  29. Science. 2020 Apr 24;368(6489):395-400 [PMID: 32144116]
  30. iScience. 2024 Apr 23;27(5):109713 [PMID: 38746668]
  31. Int J Hyg Environ Health. 2021 May;234:113723 [PMID: 33690094]
  32. Korean J Radiol. 2024 Oct;25(10):865-868 [PMID: 39344542]
  33. Philos Trans R Soc Lond B Biol Sci. 2017 May 5;372(1719): [PMID: 28289265]
  34. Biom J. 2023 Dec;65(8):e2300096 [PMID: 37890279]

Grants

  1. I-5117/Austrian Science Fund (FWF) under the project GeoEpi
  2. SI2023 Awards/International Society for Photogrammetry and Remote Sensing (ISPRS) under ISPRS Scientific Initiatives

MeSH Term

Humans
Artificial Intelligence
Public Health
Forecasting
Geographic Information Systems

Word Cloud

Created with Highcharts 10.0.0modelshealthfoundationgeospatialresearchpublicGeospatialdigitalsurveillancechallengesAIgenerativepotentialfutureerarapidtechnologicaladvancementsartificialintelligencereshapingindustriesofferingnewadvancedsolutionswiderangescientificareasparticularlyenvironmentalHoweverpreviouslymostlyfocusedunderstandinggeneratingtextfeaturesinterrelationsflowscorrelationsneglectedThuspaperoutlinesimportanceFoundationModelsrevolutioniseexaminelatestadvancesopportunitiesethicalconsiderationsapplicationsfocusspecificintegratingcontextlaymultimodalvarietyavenuesassessmentrevolution:health-applicationsagentsAI-poweredGenerativeHealthLargelanguageMultimodaldataanalysis

Similar Articles

Cited By

No available data.