A Privacy-Preserving Architecture for Personal Health AI Agents: Design, Implementation, and Evaluation

Abstract

The emergence of AI agents in healthcare presents transformative opportunities for personal health management. However, current medical AI agent architectures assume cloud deployment, requiring transmission of sensitive health data to external servers. Additionally, existing health AI systems focus exclusively on Western allopathic medicine, neglecting the integrative health practices used by billions worldwide. This study presents the design, implementation, and evaluation of a privacy-preserving personal health AI agent that operates with complete data sovereignty through a novel separated data planes architecture. The system uniquely supports integrative medicine, combining Western clinical knowledge with Eastern traditional medicine systems including Ayurveda, Traditional Chinese Medicine (TCM), and evidence-based home remedies. We developed an agent architecture utilizing local language model inference (Ollama/qwen2.5:7b), vector similarity search (FAISS), and multi-user access control entirely on local infrastructure. The system was deployed in a four-member household, successfully indexing 3,424 document chunks with average response time of 28 seconds on consumer hardware (AMD Ryzen 5 Pro, 32GB RAM, no GPU). Zero bytes of personal health data were transmitted to external servers. This work establishes that privacy-preserving health AI agents are technically feasible using current open-source components. The separated data planes architecture and integrative medicine framework represent novel contributions enabling AI agent capabilities without privacy compromise while serving culturally diverse populations.

Ähnliche Arbeiten