AI & Health Symposium

Key takeaways included:

• The problem must be significant enough. Scaling often fails because a pilot only offers marginal improvement. A strong, widely felt need is crucial to drive adoption.

• Never skip the foundations. Infrastructure matters — not just in emerging healthcare systems, but also in highly established ones like the Netherlands, where complexity can slow innovation.

• Rethinking workflow and organizational structure is essential in order to pave the way for the scale up of the AI pilots. Scale up is a socio-technical change process.

• Activate multi-level learning cycles. Users must learn to work with AI and legacy systems, while developers and stakeholders must continuously receive feedback. Scaling requires ongoing socio-technical learning — not just technical rollout.

The audience consisted of people genuinely passionate about the future of AI in healthcare. After the keynotes, they engaged with a series of controversial statements — and the diverse reactions sparked energetic discussions with the panel: Dr. Anton Bouter (CWI), Dr. Willem Grootjans (LUMC), Niek Versteegde (Goal 3), Marketa Ciharova (VU Amsterdam), Walter Kroes (Pacmed), and Dr. Renate Baumgartner (VU Amsterdam).

The panelists came from different professional backgrounds (medical, research, business, and IT development) and the AI initiatives ranged from radiology to mental health, diagnostics, accessible clinical applications, and automated treatment planning — which meant each statement resonated differently. These interdisciplinary discussions showed that scaling AI depends not only on technological capability, but also on people, systems, learning processes, and context.

The AI & Health Symposium became an important step in fostering cross-community learning — successfully sparking debates around real use cases and opening discussions on the pain points that shape AI adoption from the perspective of the many parties involved.