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White paper · Executives and CIOs
Clinical AI in Hospitals: Where to Start
Prerequisites, use cases and regulatory framework: a pragmatic guide for hospital decision-makers looking to move from experimentation to value.
- Written by Galeon, the EHR of 19 hospitals in France
- PDF format, ready to share with your teams
- Free, a 5-minute read
Flip through the guide
What’s inside.
Clinical AI raises as much expectation as caution. This condensed white paper sets out a framework for decision-making: what the AI Act actually says, mature use cases, data and compliance prerequisites, and concrete recommendations, without overpromising.
- The potential and its conditions
- The prerequisites: data and governance
- Use cases: mature vs emerging
- The regulatory framework
- A realistic roadmap
Clinical AI: where to start
galeon.careContents
- 01The potential and its conditions
- 02The prerequisites: data and governance
- 03Use cases: mature vs emerging
- 04The regulatory framework
- 05A realistic roadmap
Chapter 01
The potential and its conditions
3Chapter 02
The prerequisites: data and governance
4Chapter 03
Use cases: mature vs emerging
5Chapter 04
The regulatory framework
6Chapter 05
A realistic roadmap
7Frequently asked questions
What we are asked about this.
Where do you start with a clinical AI project?
The first step is not choosing a model but structuring data at the source and setting up governance. AI trained on ambiguous or poorly coded data produces unreliable results.
What does the AI Act say for healthcare AI?
The European AI Act classifies AI systems by risk level. Not all clinical AI is automatically high risk: the classification depends on the use and its impact on the patient, and it drives obligations for documentation, oversight, and traceability.
What are the mature use cases for AI in hospitals?
The most mature uses mainly ease the documentation burden and support decision making, for example summarizing reports or assisting with coding. Clinical value is built on targeted, measurable cases rather than broad promises.
Why does data quality determine AI reliability?
A model reflects the data that feeds it, and around 80% of health data remains unstructured, which limits its use. Structuring data at the source is therefore the prerequisite for reliable, auditable AI.
How do you keep human control over clinical AI?
By keeping the physician in the loop and ensuring every clinical use remains traceable and auditable. Clinical oversight is a layer in its own right, from infrastructure to the decision.
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