Health GeoAI beyond algorithms: Embedding equity, accountability, and environmental responsibility

Abstract

Health GeoAI—the integration of artificial intelligence with geographically contextualized health data—offers transformative potential for precision public health. Yet its rapid expansion, often driven by algorithmic performance, risks reinforcing spatial inequities, obscuring decision pathways, and generating environmental externalities. This study introduces a forward-looking framework for Responsible Health GeoAI that embeds geographical equity, accountability, and environmental sustainability as core design imperatives rather than peripheral considerations. Building on advances in foundation models and multimodal learning, the framework establishes two measurable boundaries—an equity floor ensuring subgroup fairness and calibration, and a carbon ceiling constraining computational and energy costs. These operational principles align GeoAI innovation with the broader goals of fairness, transparency, and sustainability. By situating GeoAI as a socio-technical system and integrating spatial validation, participatory governance, and carbon accountability, this study provides a structured pathway for developing GeoAI that is not only intelligent but also equitable, explainable, and environmentally responsible. The framework offers strategic insights for the institutionalization of responsible AI in global health and sustainability policy.

Keywords

Health GeoAI / Responsible AI / Geographical equity / Foundation models / Digital health governance / Sustainability

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the authors used ChatGPT in order to improve the readability of the language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

CRediT authorship contribution statement

Anquan Xia: Writing - review & editing, Conceptualization. Jia-jing Xu: Writing - original draft, Conceptualization. Wangyi Shang: Investigation. Xiang Wei: Project administration, Methodology. Di Zhao: Investigation. Cheng Ma: Investigation. Xiaolan Lv: Validation. Qining Yang: Resources. Yi Xu: Writing - original draft, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the Central Fiscal Geological Survey Project (Grant No. DD20230203903). The authors sincerely acknowledge the financial support provided by this project. We also thank our colleagues and collaborators for their valuable discussions and technical assistance during the course of this research.

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