Using AI for biotech and scientific translation – with human precision

Before AI-assisted tools like DeepL and Google Translate entered the stage, translating biotech and scientific marketing content was rather time-consuming. Today, life science companies with multilingual audiences, growing product portfolios, and lean marketing teams increasingly use these tools to achieve faster translations at lower cost.

And why not? After all, the pressure to publish product documentation, application notes, and marketing collateral in multiple languages is growing, especially for companies expanding into new geographic regions. 

AI translation is a valuable tool for generating quick first drafts that can be post-edited to match tone and terminology. And thanks to recent improvements, tools like DeepL have become surprisingly accurate even with technical content. (At least that is true for the German-English language pair I am working on; I cannot tell about others.)

Still, these tools make errors, just like other AI-based tools do. Sometimes these errors are not evident at first sight, especially when the copy contains technical terms that are rarely used and have probably not been part of the tool’s training process.

Just as with any other AI-based tool, editing and proofreading by a human expert is an absolute must.

Where AI translation tools shine – and where they fail

AI tools are excellent for translating simple, repetitive, factual content, such as internal memos and documentation, FAQs, and product descriptions. AI also helps non-native speakers understand foreign-language material to get an initial grasp of a new topic.

Still, mistakes are encountered frequently, including:

• Product or company names (which usually stay untranslated)
• Wrong punctuation in numbers (1,000.00 vs. 1.000,00 for “one thousand”)
• Inconsistent use of terminology or capitalization
• Missing context: On a web product page, “cat. no.” very likely is short for “catalogue number” (and has nothing to do with feline training)

In scientific translation, even simple phrases can be misleading if they’re pulled out of context or use the wrong technical term.

And beware: When translating blog posts and social media content, the devil is in the details. Idiomatic expressions, puns, references to previous releases, and anything that relies on nuances will need extra care to avoid errors and awkward phrasing. Here, we are no longer talking about translation post-editing but transcreation. 

Post-editing: Why scientific translation needs a human touch

Even the best AI translation needs a second pair of eyes. The term “post-editing machine translation” summarizes the process of reviewing, correcting, and polishing AI-generated text to make it publication-ready. In biotech marketing, skipping this step is an absolute no-go, as it can result in scientific inaccuracies.

In addition to fixing the abovementioned issues, post-editing also includes adjusting the tonality for the respective target audience. In German, in particular, the use of either the informal “Du” or the formal “Sie” must be consistent; otherwise, it will be a reliable indicator for sloppily translated content.

Also, it is essential to ensure inclusive and gender-sensitive language. “Doctors and nurses” should not assume that the former are males and the latter are females. Additional services, such as sensitive reading, vet manuscripts for content that might upset readers in the target language – or even damage a brand.

Last but not least, German texts tend to be longer than English ones, which can be an issue in layout materials such as brochures or product catalogues. Converting lengthy, complex sentences into snappy text that fits into the designated boxes can be an art of its own.

In client projects, I never deliver a translation without post-editing. This ensures quality and shows respect for the company’s brand voice, the target audience, and the scientific accuracy biotech professionals rely on.

Best practices for high-quality AI-assisted translation

Choice of AI translation tool

DeepL and Google Translate are the most commonly used translation tools, and both have improved significantly. Recent tests show that Google Translate can now handle many idioms and compound words that used to cause problems in the past. For nuanced and highly accurate translation, especially in formal or scientific contexts, DeepL remains the preferred option. 

I heard that the variety of language pairs covered is higher in Google Translate, but that information might already be outdated. Testing both tools side-by-side on your content is often the best way to decide. However, in my experience, DeepL usually delivers more consistent results for regulated industries or marketing copy.

Create and use glossaries

Client-specific glossaries ensure consistency across documents and projects. They help avoid awkward synonyms, enforce standardized terminology, particularly for technical or branded terms, and are also helpful for future projects.

Leverage context clarifier and tonality settings features

DeepL’s tonality settings and context clarifiers help to clear up any ambiguity in tricky terms. Whether you’re translating for a business audience or a casual reader, these features reduce the need for guesswork and enable you to control the tone more effectively.

Segment complex sentences and post-edit repetitions

AI tools translate sentence-by-sentence, often leading to repetitive phrasing. Breaking up long sentences and disentangling multi-compound words – something the German language is (in)famous for – improves readability. Varying synonyms during post-editing, such as replacing repeated uses of “the data show…” with “demonstrate,” “indicate,” or “reveal,” adds flow.

Use style guides and post-editing tools

Style guides improve consistency and enforce brand tone. A good briefing meeting helps adjust the style to the targeted audience.
In addition, tools like Grammarly, DeepLWrite, or Duden support language refinement, especially when dealing with punctuation and idiomatic phrasing. While AI tools can suggest alternatives, human judgment ensures that the selected word fits the context.

What about data privacy and AI restrictions?

Some clients request that no AI tools be used in translation. This is usually due to concerns about confidentiality or IP protection, which I entirely respect. In these cases, I typically leave the creation of the first draft to the client. This way, they fully control how it is generated. 

If clients agree to using AI-assisted translation, it is always mandatory for me to:

• Use EU-based DeepL Pro instead of US-based tools to ensure data privacy 
• Stripping documents of personal or proprietary identifiers before input
• Being transparent with clients about when and how AI tools are used

I am a native speaker. Can’t I do the post-editing myself?

This is a question that I get from clients from time to time. As with writing, being a native speaker is usually not enough to become an author of compelling yet accurate content. 

Also, you need to be an expert in the field, which is why most translators not only specialize in a particular language pair, but also in a specific field: Science, Legal, Technical, or Literature translation. As a trained scientist, I would not take on a project involving post-editing a business contract or science-fiction novel; instead, I would find an expert within my network to handle it.

Whether it’s brochures, research-driven blog posts, or product pages, scientific translation requires care. In biotech translation especially, clarity and tone can make the difference between gaining trust – or raising doubts.

Disclaimer: While I recommend DeepL in this article, I want to clarify that I have no financial relationships or affiliations with the company. My endorsement is based solely on my personal evaluation and opinion.

Image: Trid India on Pixabay.