Digital Health in the Netherlands 2026: Team Design for Imaging, Diagnostics, and Data Platforms

Across Dutch hospitals, diagnostics companies, and digital health scale-ups, AI is increasingly embedded in:

• Imaging workflows (radiology, pathology, cardiology).
• Diagnostic support and triage.
• Population health and operational optimisation platforms.

Investment is strong, but pressure is rising: AI Act alignment, workforce shortages, and clinical adoption challenges converge just as systems move toward production.

Most organisations still rely on hero data scientists and over-extended clinical champions.

Common scenarios we see across companies:

• One or two data scientists supporting multiple imaging tools.
• Radiologists asked to “own AI” on top of full clinical workloads.
• Fragmented data platforms with no clear product ownership.
• Slow transition from pilot to CE-marked, operational systems.

The hidden problem: teams are optimised for experimentation, not regulated delivery.

High-performing Dutch organisations are converging on clearer team shapes.

1. Imaging AI teams

• ML engineer with regulated-environment experience.
• Data engineer focused on imaging pipelines.
• Product owner fluent in clinical and regulatory constraints.
• MLOps Engineer.

2. Diagnostics data platform teams

• Platform owner with authority over roadmap and standards.
• Data architect.
• Interoperability engineer (FHIR, EHR, device integration).
• Data governance lead aligned to AI Act and MDR requirements.

3. Hospital digital health core teams

• Digital product leadership.
• Data & AI leads.
• Change management and clinical adoption specialists.
• CMIO" to bridge clinical workflows, IT and AI adoption

The pattern is consistent: clear ownership beats raw technical talent.

By 2026, several profiles move from “nice to have” to essential:

• Clinical data scientists who understand imaging, diagnostics, and validation.
• ML engineers and MLOps specialists are comfortable with auditability and monitoring.
• Data platform engineers who can handle interoperability at scale.
• Digital health product owners with genuine clinical literacy.

These profiles are scarce in the Dutch and wider EU market, particularly those who combine clinical context, engineering depth, and regulatory awareness.

The most realistic staffing strategies we see blend:

• A permanent core for ownership and continuity.
• Interim specialists to accelerate regulated builds or migrations.
• Specialist recruitment partners who understand both AI and life sciences constraints.

Trying to staff everything permanently, upfront, often slows progress more than it helps.

1. Audit current teams against regulated delivery, not pilots.
2. Clarify ownership for imaging, diagnostics, and platforms.
3. Align AI initiatives with AI Act and MDR timelines.
4. Redesign roles before hiring headcount.
5. Use interim talent strategically to unblock scale.
6. Build clinical adoption into team structures, not afterthoughts.

At Panda Intelligence, we work daily with Dutch teams navigating this transition. The leaders who succeed by 2026 are not chasing more AI; they are building teams designed to carry it responsibly into care.