AI, Risk, and Trust in Life Sciences Translation
When Fluency Masks Patient Risk
(with Andres Heuberger)
In the latest episode of Localization Fireside Chat, I sat down with Andres Heuberger, VP of Sales and Marketing at Language Scientific, to tackle a topic that should be top of mind for anyone working at the intersection of regulated content and AI: the real risks that come with AI-generated translation in life sciences, and why fluency does not equal safety.
This conversation was not about AI hype. It was about risk management, systems, and trust. Andres brought clarity to what many organizations still get wrong when they adopt machine translation or generative models in regulated workflows.
Why Life Sciences Translation Is Not Just “Translation”
In domains like clinical trials, regulatory submissions, and medical labeling, language is not cosmetic. It is operational. Mistranslation in these contexts can affect regulatory compliance, clinical outcomes, and ultimately patient safety.
Andres emphasized that life sciences translation must be treated as a quality system, not a commodity. While general translation tools can produce “fluent” output, fluency alone is insufficient when the cost of error is measured in regulatory rejection or patient harm.
Translation in this domain requires:
Domain-specific expertise in medical and technical terminology
Human review workflows tailored to regulated content
Quality controls that go beyond surface readability
Alignment with global regulatory expectations
AI can assist, but it cannot replace these systems.
Language Scientific’s approach blends AI with human expertise. They deploy AI where it accelerates repetitive tasks while keeping humans in the loop for judgment, accountability, and safety checks.
The Big Misconception: Fluency Masks Risk
One of the central takeaways from this episode is that the industry often equates fluency with quality. That is a dangerous assumption.
A machine can generate text that looks perfect but misunderstands domain context, regulatory nuance, or industry-specific terminology. In life sciences, that gap between surface fluency and true domain accuracy is where risk lives.
Andres stressed that AI, when used without proper controls, can introduce errors that are systematically invisible until it is too late, whether in regulatory submissions, clinical trial documentation, or patient-facing materials.
AI does not “understand” risk. It optimizes for patterns. Without rigorous quality systems, that optimization can create a false sense of safety.
Trust Is the Hard Part
AI adoption in life sciences is not primarily a technology problem. It is a trust problem.
Even when AI tools improve speed or cost, organizations hesitate because the stakes are too high. Trust is not built through marketing claims. It is built through process transparency, auditability, and measurable quality outcomes.
Teams evaluating AI translation tools should ask:
What quality controls are enforced?
How is domain expertise embedded in the workflow?
How are errors tracked and mitigated?
Does the process align with regulatory expectations?
If the answer is “we rely on AI confidence scores” or “human reviewers just read it,” that is not enough. Trust must be engineered, not assumed.
Where AI Actually Helps, and Where It Does Not
AI is useful, but only in the right parts of the workflow. Andres highlighted areas where AI adds real value:
Terminology extraction and consistency checking
Draft generation to accelerate reviewer workflows
Pattern recognition across large document sets
However, AI becomes unsafe when it is:
Given full autonomy without human oversight
Used as a substitute for domain expertise
Trusted to interpret regulatory nuance without specialist review
In life sciences, AI should be treated as a tool inside a controlled system, not a standalone solution.
What This Means for Leaders
If you lead clinical, regulatory, or localization teams, you need to reframe how you think about AI in language workflows.
AI should never be the endpoint. It should be a component inside a quality system designed for risk management.
Organizations often chase speed and cost savings. Andres argues that quality and safety must come first, because in life sciences, language errors are not harmless. They are operational and can impact patient outcomes.
Leaders should evaluate AI tools not by fluency or benchmark scores, but by how well they integrate with domain expertise, quality control, and compliance systems.
Final Takeaway
AI is transforming translation workflows. But without intentional design and a risk-oriented mindset, it will transform risk as well.
This conversation with Andres cuts straight to the core truth: fluency can mask risk, and systems matter more than models.
Robin Ayoub 
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