Guide 02 — How an AI Healthcare Application Fits Together
From healthcare data to real-world impact — the building blocks, the flow and the safeguards.
The six layers between a hospital's data and a clinical outcome, with the governance that applies across all of them.
A4 sheet — scroll sideways or pinch to zoom
Visual Guide 02 · Foundation / Technical / Builder
How an AI Healthcare Application Fits Together
From healthcare data to real-world impact — the building blocks, the flow and the safeguards.
Healthcare AI Learning
AI supports workflows and outcomes; it is not the endpoint.
Example flow
Example task (synthetic): summarise a patient's latest HbA1c in the context of diabetes follow-up.
1. Retrieve relevant patient data via an approved FHIR/API path
2. Retrieve contextual or guideline information where needed (RAG / approved source)
3. Assemble a bounded context
4. LLM creates a draft
5. Clinician reviews and validates
6. Output used in the workflow — better-informed care
Key takeaways
- It is a system, not a single technology.
- Context quality matters more than model choice.
- Healthcare adds safety, privacy and governance requirements.
- The goal is measurable real-world value, not AI for its own sake.
Related learning
Guides summarise the same primary sources cited in the related courses — official EU legal texts, standards bodies and peer-reviewed literature. Educational summaries only, not legal or clinical advice; regulatory dates were checked between 25 August and 10 September 2026.
Updated Sep 2026