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.

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Provider and deployer (AI Act roles)

The EU AI Act allocates obligations according to an operator's role in the AI value chain, defined in Art. 3. A provider is a natural or legal person, public authority, agency or other body that develops an AI system or a general-purpose AI model, or has one developed, and places it on the market or puts it into service under its own name or trademark, whether for payment or free of charge (Art. 3(3)). A deployer is any such person or body using an AI system under its authority, except where the use is in the course of a personal non-professional activity (Art. 3(4)). Importers and distributors handle systems from third-country providers, and non-EU providers of high-risk systems must appoint an authorised representative in the Union (Art. 22).

Providers of high-risk systems carry the bulk of the obligations (Art. 16): compliance with the Chapter III Section 2 requirements, quality management system, documentation, conformity assessment, CE marking, registration, corrective actions, cooperation with authorities and post-market monitoring. Deployers (Art. 26) must use systems according to the instructions, ensure human oversight by competent persons, ensure relevant input data, monitor operation, keep logs, inform workers and affected persons, and in some cases perform a fundamental rights impact assessment. Under Art. 25, a deployer, distributor or importer becomes a provider, taking over all provider obligations, if it puts its own name or trademark on a high-risk system, makes a substantial modification to it, or modifies the intended purpose of a system (including a non-high-risk one) such that it becomes high-risk; the original provider must then cooperate and hand over documentation.

Life sciences companies often occupy several roles at once. A MedTech company developing an AI diagnostic is a provider; the hospital using it is a deployer; a pharmaceutical company that fine-tunes a general-purpose model into a pharmacovigilance case-processing tool and uses it internally is both the provider (it developed the system) and the deployer, even though it never sells it; a CRO running a vendor's AI-based patient recruitment tool for a sponsor is a deployer, and possibly a provider if it rebrands or repurposes it. Role determination should be documented for each system in the AI inventory, in parallel with the controller or processor analysis under the GDPR, since the two frameworks allocate responsibilities on different criteria (control over the system versus control over purposes and means of data processing). iliomad's AI compliance services include role mapping and contractual allocation along the value chain.