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 can improve diagnostics, treatment recommendations, and patient monitoring but without safeguards it can be manipulated. Adversarial attacks on medical imaging AI have been shown to cause misclassifications (European Journal of Radiology).
The EU recognises this: under the AI Act, most health AI is “high-risk” and must meet requirements for risk management, logging, transparency, and human oversight (European Commission).
What makes healthcare AI especially vulnerable?
High-value data: medical records and biomarkers can be monetised.
Legacy IT systems: hospitals often run outdated software.
Safety-critical use cases: an AI mistake can harm patients.
A striking example: the WannaCry ransomware attack (2017) disrupted the UK NHS, cancelling appointments and locking critical systems (UK National Audit Office).
What regulations apply to AI in healthcare in Europe?
AI Act (2024) high-risk AI systems must comply with strict risk, logging, and oversight rules (European Commission).
MDR/IVDR safety and performance rules for devices, including AI-powered ones.
NIS2 Directive (2023) cybersecurity rules for hospitals and health infrastructure (European Commission).
European Health Data Space (EHDS) secure EU-wide health data access and exchange from 2025 (European Commission).
What real-world health data breaches should I know about?
Flo Health (2021): settled with US FTC for sharing sensitive reproductive data without consent (FTC).
Flo Health (2025): faced new lawsuits; a California jury also found Meta liable for illegally collecting Flo users’ menstrual data (Reuters).
These cases underline that health data is both sensitive and heavily scrutinised.
What can startups do to avoid AI security pitfalls?
Secure training data integrity
Audit trails from day one
Adversarial testing
Incident response plans
Data Protection Impact Assessments (DPIAs) under GDPR
Investors increasingly check these; a weak security posture is becoming a deal-breaker.
Can Europe lead on AI security in healthcare?
Yes, if it turns regulation into a competitive advantage.
Europe’s bet is that “trustworthy AI” will attract hospitals, regulators, and patients. If secure-by-design becomes the norm, EU firms may gain a global edge, provided compliance doesn’t strangle startups.
In healthcare, AI is only as valuable as it is trustworthy. Europe is trying to legislate that trust into existence.
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:
