Can I use machine translation for instructions for use?

There is no explicit prohibition of machine translation in the MDR. What matters is that the final result is accurate, comprehensible, and regulatory compliant. In practice, this means machine translation can be used as a pre-translation but must be reviewed and revised by qualified specialist translators (Machine Translation Post-Editing, MTPE). Responsibility for accuracy always lies with the manufacturer.

How does AI translation quality compare to human translation?

Modern neural machine translation systems achieve remarkable quality for general texts. However, for highly specialized medical device content, quality drops significantly — especially for rare language pairs, complex terminology, and safety-critical instructions. Studies show that MTPE can reduce turnaround times by 30-50% compared to pure human translation, but quality assurance must be guaranteed.

What risks does AI translation pose for medical devices?

The biggest risks are so-called "fluent errors" — translation mistakes that sound linguistically correct but are factually wrong. For medical devices, such errors can jeopardize patient safety. Additional risks include data privacy concerns with cloud-based translation services, inconsistent terminology, and lack of traceability in the audit trail.

Technologie

AI in IFU Translation: Opportunities and Limits

How Machine Translation Is Changing the IFU Workflow — and Where Human Expertise Remains Essential

9 min read

The State of AI Translation in Medical Technology

Artificial intelligence has fundamentally transformed the translation industry. Neural machine translation (NMT) systems like DeepL, Google Translate, and specialized industry solutions achieve quality levels for many language pairs that would have been unthinkable just a few years ago. For the medical device industry, this raises a central question: Can AI accelerate the complex and costly translation of instructions for use — and if so, under what conditions?

The answer is nuanced. AI-assisted translation offers significant efficiency gains but carries specific risks in the regulated medical device environment that must be carefully managed. Responsible use of AI requires a clear understanding of both the technological possibilities and the regulatory framework.

Opportunities: Where AI Provides Real Value

Accelerated Pre-Translation

The greatest advantage of AI translation lies in pre-translation. Instead of starting with a blank document, specialist translators receive a machine-generated draft to revise and validate. This approach — known as Machine Translation Post-Editing (MTPE) — can reduce translation time by 30-50%, particularly for structured, repetitive texts such as safety warnings or cleaning instructions.

For manufacturers with large product portfolios and frequent IFU updates, MTPE offers enormous savings potential. The prerequisite, however, is that the machine pre-translation delivers sufficient baseline quality — otherwise, post-editing becomes more labor-intensive than translating from scratch.

Consistency Checks and Quality Assurance

AI systems excel at automated quality checking: detecting terminological inconsistencies, missing translations, numerical errors, incorrectly converted units of measurement, and formatting deviations. These checks can run in parallel with the translation process and identify errors early.

manualworks uses AI-powered quality checks to automatically detect exactly these types of errors — before they reach the approval stage. This proactive quality assurance not only saves correction costs but also enhances the safety of the final product.

Limits: Where AI Falls Short

Safety-Critical Content

For safety-relevant instructions — warnings, contraindications, dosage information — machine translation is particularly risky. NMT systems can produce so-called "fluent errors": translations that sound linguistically impeccable but are factually incorrect or misleading. In a medical context, such errors can endanger patients.

For example, the German instruction "Nicht auf verletzter Haut anwenden" (Do not use on injured skin) might be machine-translated with a subtly different meaning that confuses "broken" with "fractured" rather than "damaged." NMT systems often fail to recognize such nuances. The distinction between "broken" meaning "injured" versus "fractured" is self-evident for a human specialist translator but represents a significant challenge for a machine.

Rare Language Pairs and Specialized Terminology

The quality of machine translation varies significantly by language pair. While English-German or English-French yield good results, quality drops considerably for language pairs such as German-Estonian or German-Maltese. Precisely the "smaller" EU languages needed for MDR compliance are often inadequately covered.

Additionally, NMT systems frequently struggle with highly specialized terminology. Medical device terms that rarely appear in general training data are often translated incorrectly or not at all.

Regulatory and Data Privacy Considerations

Using cloud-based translation services raises data privacy questions. When unpublished product information is transmitted to external servers, risks arise regarding trade secret protection and GDPR compliance. Furthermore, most AI translation services lack the traceability and documentation required under ISO 13485.

Manufacturers should ensure that AI tools deployed meet data privacy and documentation requirements. On-premise solutions or specialized platforms like manualworks offer clear advantages over general cloud services in this regard.

The Optimal Approach: Combining Human and Machine

The future of IFU translation lies not in either-or but in the intelligent combination of AI and human expertise. A proven approach is the three-stage model:

First: AI-assisted pre-translation using a system trained on medical terminology. Second: Post-editing by qualified specialist translators with experience in medical technology. Third: Regulatory review by a Regulatory Affairs Specialist or subject matter expert in the target market.

This model combines the speed of AI with the precision of human experts and the regulatory assurance of a structured review process. It enables significant efficiency gains without compromising quality or regulatory compliance.

Conclusion

AI-assisted translation is not a silver bullet for IFU localization, but it is a valuable tool within the overall process. The key is to deploy AI where it has strengths — pre-translation, consistency checking, quality assurance — and to preserve human expertise where it is indispensable: in specialist validation, regulatory review, and responsibility for patient safety.