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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.

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AI Officer

An AI Officer is the individual or outsourced function designated by an organisation to lead its governance of artificial intelligence: ensuring compliance with the EU AI Act, the GDPR and sector-specific rules such as the MDR, and embedding responsible AI practices across the AI lifecycle. Unlike the Data Protection Officer, the AI Officer is not a role mandated by EU law; the AI Act instead imposes obligations on providers and deployers as organisations, requires AI literacy of staff (Art. 4) and, for high-risk providers, a quality management system (Art. 17). Many organisations nevertheless create the role, sometimes called Chief AI Officer, Head of AI Governance or Responsible AI Lead, to give these obligations an owner, and ISO/IEC 42001 expects top management to assign responsibilities and authorities for the AI management system.

Typical tasks include maintaining an inventory of AI systems and models in use or development; classifying each under the AI Act (prohibited, high-risk, transparency-only, minimal risk; provider or deployer role) and under sector law; overseeing risk management, data governance, technical documentation, human oversight and post-market monitoring for high-risk systems; coordinating FRIAs and DPIAs with the DPO; setting policies for staff use of generative AI tools; managing relationships with model providers and vendors; ensuring AI literacy training; reporting to management and boards; and acting as contact point for market surveillance authorities, notified bodies and the AI Office. In pharmaceutical companies the AI Officer also aligns with EMA and FDA expectations on AI in the medicinal product lifecycle and with GxP validation.

The AI Officer and the DPO are distinct but closely linked roles: the DPO's independence and statutory tasks under Art. 37 to 39 GDPR must be preserved, and the DPO cannot also be the person deciding on purposes and means of AI processing, whereas the AI Officer often has an operational mandate. Small and mid-sized life sciences companies frequently outsource both to the same provider to ensure coherence between AI Act and GDPR documentation. iliomad offers an outsourced AI Officer service and acts as EU AI Act authorised representative for non-EU providers.