Navigating Europe's AI regulatory labyrinth: A strategic guide for US pharma innovators

Artificial intelligence (AI) is quietly reshaping pharmaceutical research. Driven by promises of faster trial design and drug discovery, the global AI-in-pharma market is projected to grow over 40% annually through 2030.

Yet, for US drug developers, bringing these tools across the Atlantic introduces steep regulatory hurdles. European health authorities are increasingly strict about how companies train algorithms, protect patient data, and prove the reliability of AI-generated clinical evidence.

Because all the top US biopharmaceuticals run clinical trials in Europe, ignoring EU regulatory shifts isn't an option. Here is how US compliance and R&D teams can build AI pipelines that satisfy European regulators without stalling trial launches.

European regulations: A layered architecture

Since taking effect in August 2024, the EU AI Act (Regulation (EU) 2024/1689) has categorised AI tools by risk level. For pharma developers, any AI system used to screen patients, recommend treatment protocols, or identify safety issues in a trial will almost certainly qualify as a high-risk AI system.

This triggers heavy obligations before a trial even starts. Companies must maintain comprehensive technical documentation, establish quality management systems, conduct conformity assessments, prove human oversight, and register the system in the EU database.

On top of the AI Act are three more regulatory layers:

• EMA Expectations: The European Medicines Agency’s (“EMA”) 2024 Reflection Paper on the use of AI in the drug lifecycle added sector-specific expectations on top of the AI Act's general requirements. Pharmaceutical companies must demonstrate model design transparency, data representativeness, and meaningful human oversight throughout the product lifecycle.
• Medical Device Regulation (MDR): If an AI system directly influences clinical decision-making, such as in patient stratification or dosing, it may qualify as a medical device, triggering parallel certification with its own conformity assessment, clinical evidence, and post-market surveillance obligations.
• GDPR: Because pharmaceutical AI processes health and genetic data, the GDPR applies concurrently, requiring a lawful basis for processing, data protection impact assessments, and compliance with Article 9 sensitive data rules.

The result is that a single AI system may simultaneously be subject to the AI Act, the MDR, the GDPR, and EMA sector guidance – each imposing distinct but overlapping obligations.

Data governance: What regulators expect

US teams deploying AI in European trials face three immediate operational challenges:

1. The ‘Explainability Imperative’

The EMA expects trial sponsors to explain exactly how their AI models work. Before launching a trial, they must: demonstrate training data sources; validate datasets for bias; explain model outputs; provide human override controls; and track performance over time. Ultimately, legal and ethical responsibility stays with the trial sponsor, not the algorithm. 

2. Resolving the consent trap

Under the GDPR, it can be challenging to establish a clear legal basis for processing clinical data. Often, relying on consent under Article 6(1)(a) GDPR has created a ‘double consent requirement’ alongside standard trial participation consent. The draft EU Biotech Act (December 2025) aims to fix this by shifting the legal basis to compliance with a legal obligation (Article 6(1)(c) GDPR), backed by public interest provisions under Article 9(2)(i) GDPR. This would eliminate duplicate consent paperwork for trial teams.

3. Using synthetic data safely

Synthetic data addresses a twofold need: overcoming quantitative limitations of clinical datasets and enabling AI model development, whilst minimising real personal data use. Both the EMA Reflection Paper and the AI Act recognise synthetic data as a relevant data augmentation technique. However, models trained on datasets that fail EU representativeness and bias standards will be rejected. A DPIA must be conducted before any large-scale health data processing for AI training.

Local privacy variations: Design for divergence early

Since 2018, European regulators, data authorities, and ethics boards have interpreted the GDPR differently. Additionally, EU law permits member states to add extra restrictions on sensitive health data, creating a patchwork of conflicting local rules.

The proposed Biotech Act (expected adoption 2027) would block individual countries from piling on extra data or consent requirements under the EU Clinical Trials Regulation (CTR), finally ending the need for country-by-country legal reviews. But, until the Act becomes law, local checks remain critical, and pharma companies that build them into their trial protocols from day one will avoid expensive delays.

Actionable strategy for US R&D and compliance leaders

To keep trials moving while meeting European standards, leadership teams should focus on six core priorities:

1. Classify AI systems before you design your trial

The single most important step is to perform an AI risk classification exercise at the outset. This should assess whether the tool is high-risk under the EU AI Act, what data it processes, whether it meets MDR medical device criteria, and which obligations apply at each layer.

2. Build GxP-aligned AI governance from day one

Companies that structure AI development within GxP-aligned frameworks from day one will find the transition to EU requirements substantially smoother. This requires: version control and model documentation sufficient to reconstruct the full development history; clear validation acceptance criteria; formal change control procedures governing any modification; and comprehensive audit trails of AI decisions.

3. Establish a Data Protection Impact Assessment ("DPIA") program that covers AI systems

Every US pharma company deploying AI systems to process health or genetic data in Europe should have a formal DPIA program, addressing the nature, scope, and purposes of processing; training data provenance and representativeness; automated decision-making risks; technical and organisational mitigations; and residual risk assessment.

4. Leverage trusted testing environments and early regulatory engagement

The draft Biotech Act allows the European Commission to designate certain EU projects as strategic biotech projects, granting access to trusted testing environments – regulatory sandboxes – where teams can build and test AI tools under clear EU rules. Developers should consult regulatory authorities early to gain prospective regulatory clarity.

5. Leverage the secondary use framework

EDPB Guidelines 1/2026 reaffirm that secondary research benefits from the compatibility presumption under Article 5(1)(b) GDPR. The draft Biotech Act also permits data reuse across trials by the same controller. US pharma companies should proactively structure data agreements to leverage this framework.

6. Build an integrated compliance framework across all four regulatory layers

The best response to Europe's layered architecture is to stop treating its component instruments as separate compliance workstreams. Map GDPR, AI Act, EHDS, and sector-specific regulation against each AI system holistically, assigning clear ownership across functions, and building compliance triggers into the development lifecycle.

The regulatory framework continues to evolve

Looking ahead, the broader architecture is still coming together. Several developments are on the horizon, from the final adoption of the EDPB Guidelines on scientific research to the final approval of the recently published EDPB Guidelines 2/2026 on anonymisation. As the European Health Data Space (EHDS) takes shape and reliance on federated research infrastructure grows, the proposed Biotech Act – if adopted as proposed – will fill a critical legal gap, establishing a clear framework for GDPR-compliant AI in pharma.

The competitive advantage of regulatory readiness

European AI regulation in pharmaceutical development is demanding, but the January 2026 EMA-FDA joint Guiding Principles confirm that transatlantic regulators are actively working to align their expectations.

Rather than viewing European rules as a hurdle, US drugmakers should see them as a quality benchmark. Companies that invest now in building transparent, compliant, and well-documented AI pipelines will gain a decisive edge in bringing next-generation therapies to global markets.

About the authors

Jeremy Maltby is a partner at Portolano Cavallo, where he advises US, Italian, and international clients on complex regulatory, compliance, and dispute matters, with a strong cross-border focus on Italy. His practice includes white-collar investigations, corporate compliance, and technology-related legal risk, including AI, cybersecurity, and data privacy. Maltby has held various senior legal roles in the US Department of Justice and the White House Counsel’s Office, and previously practiced for twenty years at O’Melveny & Myers, where he served as managing partner of its Washington, DC office. He works extensively with clients in life sciences and healthcare, helping companies navigate high-stakes governance and enforcement issues arising from new AI rules in Italy and the EU. Maltby received his BA magna cum laude in History and Literature from Harvard College, before earning his JD from Columbia Law School, where he was a Chancellor James Kent Scholar and an articles editor of the Columbia Law Review. Before entering private practice, Maltby served as a law clerk to Justice David H. Souter on the US Supreme Court.

Laura Liguori has been a partner at Portolano Cavallo since 2007, where she is one of Italy’s leading advisers on data protection, digital regulation, and emerging technology. For more than 25 years, Liguori has counselled Italian and international clients on cybersecurity, data protection, internet and e-commerce law, and AI, with deep experience in both compliance strategy and contentious matters. Her work is especially focused on digital media technology and life sciences, including clinical trial and data governance issues. She graduated cum laude from the Luiss Guido Carli of Rome in 1996, with a dissertation on the first Italian law on data protection and privacy. Liguori is currently immediate past president of the ITechLaw Association and vice president of the Women&Tech Association, being consistently ranked by top legal directories, including Legal 500 and Chambers, for her leadership in data protection and technology law.

Elisa Stefanini is a partner at Portolano Cavallo and co-head of its Life Sciences-Healthcare and Public Law teams. Consistently recognised by directories such as Chambers and Legal 500 for her life sciences regulatory practice, Stefanini advises on pharmaceuticals and medical devices, with particular experience in clinical trials, market access, and digital projects in the healthcare sector. Stefanini earned her law degree cum laude from Università Commerciale Luigi Bocconi in 2004, before completing a postgraduate diploma at the Academy of European Law in 2006 and receiving a PhD in Constitutional Law from the University of Milan in 2008. She is officer at the International Bar Association’s Healthcare & Life Sciences Law Committee and vice present at the Healthcare & Life Sciences Commission of the International Association of Joung Lawyers (Aija).