Every health AI model is a decision engine — and an attack surface.
The Risks (with Evidence)
Adversarial examples derail medical imaging AI — systematic review across radiology (European Journal of Radiology).
Data poisoning, inversion & extraction are recognised clinical AI risks with mitigations like audit trails and continuous monitoring (García-Gómez et al.).
Why Healthcare Is Special
High stakes, legacy networks, and fragile systems — the WannaCry ransomware attack disrupted NHS care at scale (UK National Audit Office).
Framework for Defence
Threat modelling & asset inventory
Data integrity controls
Access isolation
Logging & audit trails
Drift monitoring
Adversarial testing
Rollback plan
Aligned with the EU AI Act’s high-risk obligations: risk management, logging, human oversight (European Commission).
In healthcare, AI isn’t “just software” — it’s safety-critical infrastructure.
Your pacemaker is now an endpoint. Attackers read release notes too.
Why Devices + AI Are Tricky
Firmware–model coupling, edge inference, constrained compute, long lifetimes.
Risks mapped in Biasin et al.’s study on AI medical device cybersecurity (arXiv).
Case in Point
The 2017 firmware recall for ~465k Abbott (St. Jude) pacemakers shows the stakes, a patch was issued to mitigate RF cybersecurity vulnerabilities (Read more).
Regulatory Overlap
AI used for medical purposes typically lands in high-risk under the AI Act, layering obligations on top of MDR/IVDR (European Commission).
This includes logging, robustness, and human oversight.
Secure Design Patterns
Isolation/sandboxing
Secure boot + model integrity checks
Fail-safe fallback modes
Lightweight cryptography
Device logging & anomaly detection
OTA updates with rollback
Adversarial robustness testing
Ship devices with a patch plan, audit trail, and model provenance. Or don’t ship at all.
AI in healthcare is often sold as a story of improved diagnostics, personalised therapies, and predictive medicine. But beneath that dream lies a fragile backbone: security. One breach, one exploited model, and reputations, finances, even lives are at stake.
In Europe, this tension is amplified. The Artificial Intelligence Act entered into force on 1 August 2024, putting health AI under new obligations (European Commission). At the same time, NIS2 extends cyber resilience rules to hospitals, while the European Health Data Space (EHDS) (in force from March 2025) will demand interoperable, secure data exchange.
This series of posts dissects that tension from five angles:
A crisp week: AI diagnostics raised, sports concussion wearables funded, a Dutch conversational-AI startup got scooped up, and the UK nudged its devices policy closer to home care.
Sports Impact Technologies (Ireland): €650K Pre-Seed for behind-the-ear concussion-detection wearable; beta with athletes kicks off in September, full launch targeted for 2026.
Better Medicine (Estonia): €1M Pre-Seed to expand CE-certified AI for kidney cancer detection, fund EU rollout and FDA-aligned pilots.
Automated insulin delivery — Utrecht’s ViCentra says its next-gen closed-loop Kaleido system is slated for a Europe launch next year, signaling more AID competition on the continent.
UK devices policy — MHRA opens a stakeholder survey on the Health Institution Exemption (HIE), floating extensions to community/home use and tighter PMS/governance—practical for hospital “in-house” SaMD/device teams.
Macro: Italy watch — New data show Italy’s tech funding momentum; healthtech has already raised ~$126M in 2025, underlining ongoing digital health demand.
One thing to remember
AI-heavy workflow tools are getting their first cheques (imaging, concussion safety) while cross-border consolidation (Caro→HOPCo) accelerates go-to-market—set against a UK policy tweak that could legitimize more hospital-built software/devices beyond the hospital walls. If you’re raising: show path to deployment (pilots, CE status) and a plan for integration into care pathways.
