Resch Bernd, Kolokoussis Polychronis, Hanny David, Brovelli Maria Antonia, Kamel Boulos Maged N
IT:U Interdisciplinary Transformation University, 4040, Linz, Austria.
Center for Geographic Analysis, Harvard University, Cambridge, MA, 02138, USA.
Int J Health Geogr. 2025 Apr 2;24(1):6. doi: 10.1186/s12942-025-00391-0.
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.
在技术快速进步的时代，生成式人工智能和基础模型正在重塑各行业，并在广泛的科学领域提供新的先进解决方案，尤其是在公共卫生和环境卫生领域。然而，基础模型此前大多专注于理解和生成文本，而地理空间特征、相互关系、流动和相关性却被忽视了。因此，本文概述了地理空间基础模型研究的重要性，这些模型有可能彻底改变数字健康监测和公共卫生。我们研究了地理空间基础模型在数字健康研究和应用方面的最新进展、机遇、挑战以及伦理考量。我们关注将地理空间背景与基础模型相结合的具体挑战，并阐述了多模态地理空间基础模型在数字健康监测和健康评估的各种研究途径方面的未来潜力。
