A Distributed Cloud-Edge Infrastructure for Equitable Healthcare: Scaling Privacy-Preserving LLMs to Identify Regional Care Disparities
Abstract
The persistence of regional healthcare disparities remains a critical challenge for global public health, exacerbated by the fragmentation of medical data and the limitations of centralized analytical models. While Large Language Models (LLMs) offer transformative potential for synthesizing unstructured clinical notes and identifying social determinants of health, their deployment is often hindered by stringent privacy regulations and the computational bottleneck of centralizing sensitive patient records. This paper proposes a distributed cloud-edge infrastructure designed to facilitate equitable healthcare by scaling privacy-preserving LLMs across geographically dispersed clinical environments. We introduce a tiered architectural framework that leverages edge computing to perform local, privacy-compliant data processing, while utilizing a secure cloud orchestrator for global disparity synthesis. Our analysis focuses on the system-level trade-offs between local inference latency, global model coherence, and the robust enforcement of patient confidentiality. We examine the socio-technical dimensions of this infrastructure, including algorithmic fairness in underrepresented regions, the environmental sustainability of distributed medical AI, and the policy implications for multi-jurisdictional healthcare governance. By integrating federated learning protocols with hardware-verified security, the proposed framework provides a scalable roadmap for identifying and mitigating care inequities without compromising data sovereignty. The discussion concludes with a forward-looking perspective on the ethics of automated health equity assessments and the evolving regulatory landscape surrounding decentralized medical intelligence.
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