ArticleEnvironmental health : a global access science source2018
Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology.
Article in Environmental health : a global access science source, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed.
- Emerging AI tools for geospatially resolved cardiovascular risk.Nature reviews. Cardiology · 2025Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- A review of geospatial exposure models and approaches for health data integration.Journal of exposure science & environmental epidemiology · 2025Review
- Artificial Intelligence in Biomedical Engineering and Its Influence on Healthcare Structure: Current and Future Prospects.Bioengineering (Basel, Switzerland) · 2025Review
- Perspective on the ethics of AI at the intersection of nutrition and behaviour change.Frontiers in aging · 2025Article
- From infectious diseases to chronic diseases: the paradigm shift of spatial epidemiology in disease prevention and control.Frontiers in public health · 2025Review
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Privacy-preserving decentralized learning methods for biomedical applications.Computational and structural biotechnology journal · 2024Review
- Active workplace design: current gaps and future pathways.British journal of sports medicine · 2024Article
- Opportunities and shortcomings of AI for spatial epidemiology and health disparities research on aging and the life course.Health & place · 2024Article
- Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- The leading global health challenges in the artificial intelligence era.Frontiers in public health · 2023Review
- Predicting walking-to-work using street-level imagery and deep learning in seven Canadian cities.Scientific reports · 2022Article
- Transforming Social Determinants to Educational Outcomes: Geospatial Considerations.Healthcare (Basel, Switzerland) · 2022Article
- Review
- Discovery of new Cdc2-like kinase 4 (CLK4) inhibitorsRSC advances · 2022Article
- Curating maternal, neonatal and child health (MNCH) datasets from a hospital's catchment area in Nigeria between 2014 and 2019.F1000Research · 2022Article
- The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.Artificial intelligence review · 2022Article
- Blockchain and artificial intelligence technology in e-Health.Environmental science and pollution research international · 2021Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.