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

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.