^{Published on 8.1.2026}

For many companies developing medical devices that feature AI or machine learning, expanding from the European market to the United States is an exciting milestone. It’s also a move that often raises important questions, and one we at Innokas most commonly hear is

“What do we need to consider when entering the U.S. market?”

While the U.S. and EU share many regulatory principles, there are environmental, use and user related differences that can affect how your device needs to be designed, tested, and validated. Here are the key areas to keep in mind as you prepare for FDA pathways by QA/RA specialist Sandra Hänninen.

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1. Start with the environment and users

One of the biggest differences between Europe and the U.S. is the healthcare environment itself.

Where many EU healthcare systems are publicly funded, the U.S. healthcare system is largely privatized. This caninfluence everything from treatment pathways to the settings in which your device will be deployed.

Another important factor is who will be using your device. For example, In the EU, your device may be operated primarily by a nurse, while in the U.S. the same device may be used by a physician or a technician. This shift in user roles can influence how the device is perceived, how intuitive it needs to be, what training materials must include, and what risks need to be addressed in your documentation.

Understanding these differences early allows you to shape your usability engineering, labelling, and risk management accordingly.

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2. Pay close attention to your training and testing data

For any device involving AI or machine learning, data forms the cornerstone of FDA evaluation.

The FDA expects your validation to include patient data from the U.S. population or, at minimum, a strong justification for why external data is representative of the U.S. population.

You’ll need to demonstrate:

• How demographics compare

• Why your dataset is clinically relevant

• That performance in the U.S. population is consistent with your EU results

If your training data originates from outside the U.S., you need to show your device’s performance with U.S. data during validation.

The good news is, according to the FDA’s AI/ML draft guidance from January 2025, you don’t need to retrain your algorithm specifically for the U.S. population as long as its performance is adequate. This can significantly streamline your pathway if your model generalizes well.

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3. Revisit your cybersecurity documentation

Even if your device is already certified under MDR or IVDR, don’t assume your cybersecurity documentation meets FDA requirements.

While Europe and the U.S. share similar objectives, FDA guidance tends to be more granular, prescriptive and focused on ongoing monitoring and threat mitigation.

It’s common for companies to update or expand their cybersecurity documentation when preparing for FDA submission. Building in time for these updates and ensuring they reflect real-world U.S. environments helps avoid surprises later in the process.

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Smooth navigation leads to successful market entry

Bringing your AI-driven medical device from Europe to the United States is absolutely achievable. By understanding the differences in healthcare settings, validating your data appropriately, and aligning your cybersecurity documentation with FDA expectations, you'll be well positioned for success.

With the right preparation, the U.S. market can offer enormous opportunities, and your device can make a meaningful impact for clinicians and patients alike. If you could use support while making this transition, please fill in the contact form with descriptive information on your project and we’ll get back to you.