A 6-question self-assessment: gauge your organisation's AI maturity and leave with your priorities.
A quick self-assessment (use cases, data, governance, interoperability, sovereignty, skills) that returns an AI maturity level and a roadmap.
It is the organisation's ability to move from experimentation to AI use cases genuinely in production, governed and value-creating. It is assessed across several axes: deployed use cases, data quality and structuring, governance, interoperability, sovereignty, and team skills.
The first step is not choosing a model but structuring data at the source and setting up governance. Aim for a targeted, measurable use case, such as documentation or coding assistance, rather than a broad promise.
No. The European AI Act classifies systems by risk level, and not all clinical AI is automatically high risk: it depends on the use and its impact on the patient. Documentation, oversight and traceability obligations follow from that classification.
A model reflects the data that feeds it, and a large share of health data remains unstructured and therefore hard to use. Structuring data at the source is the prerequisite for reliable, auditable AI.
Yes. Governance clarifies who validates uses, how performance is monitored, and how the regulatory framework, including the AI Act, is met. Keeping the physician in the loop and making every clinical use traceable are key.
Largely. Without data that flows and is understood across systems through HL7 and FHIR standards, AI use cases stay siloed. Interoperability conditions access to structured, consistent data.
