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Cloud computing for equitable, data-driven dementia medicine
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DOI：10.1016/j.landig.2025.100902.png)
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Abstract
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Dementia poses an increasing global health challenge, and the introduction of new drugs with diverse activity profiles underscores the need for the rapid development and deployment of tailored predictive models. Machine learning has shown promise in dementia research, but it remains largely untested in routine dementia health care—particularly for image-based decision support—owing to data unavailability. Thus, data drift remains a key barrier for equitable real-world translation. We propose and pilot a scalable, cloud-based infrastructure as code solution incorporating privacy-preserving federated learning. This architecture preserves patient privacy by keeping data localised and secure, while enabling the development of robust, adaptable artificial intelligence models. Although technology giants have successfully implemented such technologies in consumer applications, their potential in health-care applications remains largely underutilised. This Viewpoint outlines the key challenges and solutions in implementing cloud-based federated learning for dementia medicine and provides a well-documented codebase to support further research.
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