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R
Randomised clinical trial (RCT)
R
Real-world data (RWD)
R
Real-world evidence (RWE)
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Recipient
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Regulatory intelligence
R
Re-identification risk (motivated intruder test)
R
Restriction of processing
R
Right not to be subject to solely automated decision-making
R
Right of access
R
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R
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R
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R
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Right to erasure ("right to be forgotten")
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S
Schrems II
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Scientific research purposes (Art. 89 GDPR)
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Secondary use of health data
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Security breach (security incident)
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Serious adverse event (SAE)
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Shadow AI
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Site management organisation (SMO)
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SNDS (Système National des Données de Santé)
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Software as a Medical Device (SaMD)
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Source data verification (SDV) and source documents
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Start of a clinical trial
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Swiss Federal Act on Data Protection (FADP / revFADP)
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Swissmedic
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Synthetic data
T
Technical and organisational measures (TOMs)
T
Temporary halt of a clinical trial
T
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Third party
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Transfer impact assessment (TIA)
T
Trial master file (TMF)
T
Trusted research environment (TRE)
U
UK GDPR
U
Unexpected serious adverse reaction (SUSAR)
U
US CLOUD Act
V
Validation (computerised system validation, CSV)
V
Vendor assessment (third-party risk management)
V
Vital interests (Art. 6(1)(d) GDPR)
V
Vulnerable participants (vulnerable subjects)
W
Wearables and digital health technologies (DHT)
W
Withdrawal (of consent and from a clinical trial)
W
World Health Organization (WHO)
X
XAI (explainable artificial intelligence)
Y
Yellow Card scheme (UK)
Z
Zero-knowledge proof
Z
Zero trust architecture
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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

C

Conformity assessment (MDR & AI Act)

Conformity assessment is the process used to demonstrate that a product or system meets the requirements applicable to it. For medical devices and high-risk AI, the required route depends on the legislation and classification. Some routes involve an independent notified body; others rely on the manufacturer’s or provider’s own documented assessment.

Key takeaways

  • Identify the applicable legislation before choosing a route.
  • Not every assessment requires a notified body.
  • Product conformity does not settle every privacy question.

MDR and AI Act Routes

The EMA medical-device overview explains assessment before CE marking. Article 43 of the AI Act connects certain product-related high-risk AI assessments with the relevant sectoral conformity procedure; it does not prescribe one identical route for every AI system.

FrameworkAssessment focus
MDRApplicable device requirements and the relevant device route.
AI ActApplicable high-risk AI requirements and the relevant AI route.
Combined productAssess how AI requirements enter the sectoral procedure.

Why It Matters for Data Protection

For an AI-enabled medical device, we recommend maintaining a requirements matrix that distinguishes product evidence from data protection decisions. Link each requirement to an owner, document and review point. Separately assess the provenance and permitted uses of training and validation data, access to participant records and supplier responsibilities. Avoid treating a conformity certificate as permission for any later use of personal data.

Hypothetical example: A manufacturer develops diagnostic software with an AI component. Before commissioning an external assessment, the team documents the intended purpose and classification assumptions. It checks which route applies, whether the chosen body has the required competence and which evidence needs updating when the model or intended use changes.

Frequently Asked Questions

Does every medical device need the same assessment?

No. The route depends on the applicable device requirements and classification.

Must every high-risk AI system use a notified body?

No. Article 43 includes routes using internal control as well as routes involving a notified body.

Can one assessment address both frameworks?

For relevant product-related AI, Article 43 provides for incorporating AI requirements into the sectoral procedure; confirm applicability and timing.

Related reading: high-risk AI systems, Medical Device Regulation. For practical support, see AI compliance services.

Updated: 27 October 2026