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🧪 AI Lab: Running Local LLMs for Healthcare Privacy

🔒 Why Local AI?

In healthcare, patient data privacy is non-negotiable. Sending sensitive medical data to cloud APIs (like OpenAI) poses significant HIPAA and compliance risks. The solution is running intelligent agents locally.

💻 The Optimization Challenge

I focus on deploying highly capable AI orchestration frameworks directly on consumer-grade hardware (16GB RAM, CPU-focused environments without dedicated GPUs).

⚙️ Current Experiments & Stack

  • Orchestration: LangGraph, CrewAI, and PydanticAI.
  • Models Tested: Qwen and Llama architectures.
  • Tools: Ollama, OpenClaw Ecosystem, and ChromaDB for vector storage.

Insights: Successfully configuring an API gateway and RAG system on this hardware demonstrates that robust, privacy-first medical AI systems are viable without massive cloud infrastructure costs.