arXiv:2605.20815v1 Announce Type: cross Abstract: Graph-based Retrieval Augmented Generation (GraphRAG) extends retrieval-augmented generation to support structured reasoning over complex corpora, but its reliability under resource-constrained, privacy-sensitive deployments remains unclear. In healthcare, where Electronic Health Record (EHR) data is complex and strictly regulated, reliance on cloud-based large language models (LLMs) introduces challenges in cost, latency, and compliance. In this work, we present a systematic evaluation of GraphRAG for EHR schema retrieval using locally deploye
The increasing complexity and regulatory demands of healthcare data, combined with a growing desire for data sovereignty and privacy, are driving the need for local AI solutions, as cloud-based LLMs present challenges in cost, latency, and compliance.
This work directly addresses the practical deployment challenges of advanced AI in highly regulated and sensitive environments, setting a precedent for 'local-first' AI strategies that reduce reliance on external cloud infrastructure.
The ability to run sophisticated GraphRAG models on consumer hardware for sensitive applications like healthcare EHR schema retrieval changes the landscape for AI adoption, making advanced capabilities more accessible and privacy-compliant for entities with resource constraints.
- · Healthcare providers
- · AI hardware manufacturers
- · On-device AI software developers
- · Patients
- · Cloud-based LLM providers (for sensitive data tasks)
- · Legacy healthcare IT systems
- · Companies with poor data governance
Increased adoption of local AI/ML solutions in regulated industries due to enhanced privacy and reduced operational costs.
Development of more energy-efficient AI models and specialized local hardware to meet the demands of on-device processing.
The emergence of new business models for 'AI-as-a-service' that prioritize data sovereignty and local processing, potentially decentralizing AI infrastructure.
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