AI in European Healthcare: From Potential to Practice
European healthcare systems are under growing pressure. Aging populations, chronic diseases, staff shortages, rising costs, and widening inequalities are challenging the sustainability of care. Artificial Intelligence (AI) is often presented as part of the solution — but in practice, its deployment remains limited.
Key takeaways
- AI can already improve healthcare, especially by reducing admin work and speeding up diagnostics
- Adoption in Europe remains slow due to data, regulation, and organisational barriers
- Low-risk AI (documentation, workflow, triage) delivers the fastest value today
- Trust, interoperability, and real-world validation matter more than new algorithms
- The EU has a unique opportunity to scale safe, ethical, and equitable AI in healthcare
Where AI already works today
A 2025 EU-commissioned study by DG SANTE examined how AI is currently used in healthcare, why adoption is slow, and what needs to change.
- Administrative tasks (clinical documentation, scheduling, billing)
- Workflow optimisation in hospitals
- Triage and prioritisation in emergency care
- Diagnostic support, particularly in radiology and cancer screening
These tools reduce administrative burden, shorten waiting times, and help clinicians focus on patients rather than paperwork.
Why deployment remains slow
- Fragmented health data and poor interoperability
- Complex regulatory landscape (AI Act, MDR, GDPR, EHDS, liability rules)
- Unclear financing and reimbursement models
- Lack of local validation and post-deployment monitoring
- Trust, explainability, and digital health literacy challenges
In many cases, AI fails not because of technology, but because of organisational and cultural barriers.
The future: from experimentation to scale
- Personalised medicine
- Predictive analytics and early risk detection
- Real-time clinical decision support
- Remote patient monitoring
The report stresses that AI must support — not replace — healthcare professionals, and always be tested in real-world clinical settings.
What the EU should do next
- Common data and interoperability standards
- Centres of excellence for AI in healthcare
- Sustainable funding mechanisms
- Mandatory local performance testing
- Continuous monitoring of AI systems in practice