AI for healthcare

AI in healthcare, with privacy first

Healthcare organisations work with special-category personal data, and that is exactly where generic AI from the United States runs into trouble. HostYourAI runs open models on GPUs in European datacenters via vLLM, so patient data never leaves the EU and processing falls fully under European law. You stay in control of which model runs, where it runs and who can reach it.

Why healthcare organisations host their AI in the EU

Data stays inside Europe

All inference happens on European GPUs. There is no US cloud provider in the chain, so you are not exposed to foreign access legislation such as the US CLOUD Act. For special-category data that is the difference between a compliance risk and a workable solution.

Open weights you can audit

You run open models such as Llama, Mistral and Qwen. No black box: behaviour is reproducible and auditable, which helps with internal review and demonstrating diligence to a data protection officer.

A Data Processing Agreement and no training on your data

A Data Processing Agreement (DPA) is available and the subprocessor list is public. Your prompts and documents are not used to train models. What you put in stays yours.

What you use AI for in healthcare

Think of summarising medical documentation and handovers, drafting clear patient information, supporting administrative text and reporting, and making internal protocols and guidelines searchable with RAG. In every case AI supports the clinician: the model delivers a draft, a human reviews and decides. Do not use it as medical advice or for autonomous diagnosis.

Healthcare requirements and how HostYourAI meets them

Certification such as ISO 27001 or a national healthcare standard sits within your own organisation and processes. We provide the building blocks that support such a track: EU processing, a DPA, isolation and transparency.

How to start

Link a knowledge base with your own protocols and guidelines; the Router automatically injects the relevant context, so answers are grounded in your sources instead of general model knowledge. Start small on the shared Router and scale to a dedicated instance once you go to production.

from openai import OpenAI

client = OpenAI(
    base_url="https://hostyourai.com/api/v1",
    api_key="hyai-...",
)

resp = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Summarise this guideline."}],
    extra_body={"knowledge_base_id": 42},
)
print(resp.choices[0].message.content)

Want to know more about linking knowledge? Read the guide Build RAG on EU GPUs.

Questions about AI in healthcare