There’s a growing use of AI for life science translation, specifically, to speed up document processing and keep up with global submissions and product launches. So far, these tools have helped many companies and researchers with efficiency. That’s a win for time saving, but quick first drafts alone don’t cut it in life sciences, where accuracy, consistency and compliance are more important than speed.
The key to getting the right quality of translation that ticks all these boxes as quickly as possible is AI translation post-editing (MTPE). How does MPTE work and how does it help to achieve quality life science translations? We’ll cover that and more here.
What Is AI Translation Post-Editing?
AI translation post-editing is the final human review and improvement process for texts that have been automatically translated by a machine. The purpose of this is to refine the generated translations to match human style and, most importantly, for accuracy.
Machine translation is best for generating large volumes of documents quickly. They, however, lack the cultural understanding, nuances, and real-life industry experience that are needed to produce a 100% compliant translation that’s ready for use.
The MTPE process always begins with a machine translation engine that’s been trained with data, glossary and other important information related to the task at hand. It then proceeds to create a draft of the needed documents.
Once that’s ready, human linguists review the output and spot every error. These aren’t grammatical most times; they are more related to word choices, contextual references, use of idioms, and formatting. So, step one focuses on getting every piece of document ready within a short time, while the second step is quality control.
It is a hybrid model that saves costs because only a small number of human translators are needed compared to when everything was done manually from first draft to QA. A few experts can review and refine as many already translated texts in a short time, depending on the quality of the output. It is also a good use of AI efficiency and human precision in sensitive content like labeling, clinical trials and regulatory submissions.
How to Ensure Quality in AI Life Sciences Translation
The best life sciences translation services have a process in place for ensuring quality in AI translations. We’ve broken these down into five easy-to-follow areas that will guarantee a final draft that’s ready to submit or use.
1. Use Domain-Trained AI Engines
Generic engines can do basic translations but can’t deliver accurate output like engines trained on life science data. These domain-trained engines learn from resources like medical literature and regulatory documents to understand the right terminology and phrasing for the field. They can easily distinguish between technical and medical terms to ensure fewer contextual errors or misinterpretations. An example is knowing how “dose” in pharmacology differs from “dosage form” in labeling.
A domain-trained AI engine isn’t so expensive either. Once the necessary training materials are available, an expert is required to train the engine using these resources. From then on, the training data can be updated often and as needed.
2. Involve Expert Human Post-Editors
Post-editing is more important than the first draft and is arguably more sensitive. You need human post editors who are experts in the field and can manually translate specific documents in the first place. They can bring their industry knowledge and experience, which machines can’t replicate, into the vetting phase.
This is very important because every single mistranslation can affect patient safety or slow down regulatory approval. To avoid potential scrutiny or expensive mistakes, human translators must verify every detail, including terminology accuracy and adherence to local regulatory language. Involving these professionals from the start can also help you determine whether or not the initial AI engine training was properly done or if an update is needed.
3. Apply Terminology Management and Style Consistency
When multilingual scientific content is needed, terminology management helps to maintain accuracy throughout. Terms, abbreviations, contextual explanations, and units of measurement often vary between countries. Whatever documents you’re creating have to be tailored for local relevance, which is why termbases, styleguides, and glossaries are a necessity.
They’ll ensure that every document follows uniform standards and matches your brand voice where applicable. These will also be part of your AI engine training resources and will act as a reference that post editors can check against the AI-generated outputs. It’s a structured approach that removes ambiguity and makes documents easily readable for researchers and other professionals who need to go through them.
4. Implement Quality Assurance (QA) and Validation
Even if AI output looks good, a strong quality assurance process confirms that it indeed meets both linguistic and regulatory standards. There are QA tools that help with basic checks like automatically picking up wrong use of terms, numerical inaccuracies, formatting errors and other inconsistencies for human review.
After automated checks, final validation involves industry and linguistic experts verifying the generated content against source documents and standard expectations. Both parts of the QA process guarantee that the translated text maintains meaning and intent without introducing risk.
5. Continuously Improve Through Feedback and AI Training
AI in life science translations is only as good as the original training information. That’s why feedback loops from reviewers and linguists are useful. They report recurring issues and suggest refinement that helps to retain AI models for better performance.
Also, regularly retraining models with approved or already edited and approved translations helps them pick up new updates like language trends or scientific terms that weren’t in their initial training.
This is a process that is repeatable till engines can deliver the best quality of translations possible. However, it is best for the training team to receive feedback from an in-house QA team early rather than leaving regulators or clients to spot avoidable inaccuracies.
Why ECI Link Is the Top Choice for Life Sciences Translation
ECI Link is our AI-powered translation platform built for life sciences. It supports pharmaceutical companies, medical device manufacturers, and research organizations that need accuracy, consistency, and regulatory readiness across multiple languages.
What makes ECI Link different is that it learns from your own approved materials. Your terminology, style preferences, and regulatory language are built directly into the system. This means your translations stay consistent across documents, regions, and projects — even as volumes scale.
Our platform combines AI speed with the expertise of linguists who specialize in life sciences. It helps reduce rework, avoid terminology errors, and keep submissions and trial documents aligned with regulatory expectations.
We’ve worked with life science teams for years, and we know what’s at stake in every document. ECI Link keeps your projects on track and helps your team work confidently, even with tight timelines and multilingual demands.
Interested in seeing how it fits into your workflow? Contact us to schedule a demo. We’ll show you how ECI Link supports high-quality translation at scale.
