Quality in the Age of AI: A Fireside Chat with Language Scientific
How AI, risk, and human expertise shape modern translation quality in life sciences.
In this exclusive fireside chat, Language Scientific dives into how AI is transforming translation quality across the life sciences. Moderated by Saad Sahai, this conversation features insights from Ashley Mondello, VP of Operations, whose decade of experience leading life science localization, AI integration, and global delivery operations offers a rare look at how quality truly works in the age of AI.
Her perspective reveals the industry’s biggest shift: quality is no longer a single gold standard—it’s a risk-based spectrum shaped by purpose, audience, and safety requirements.
Section 1: How the Industry’s View of “Quality” Has Evolved
For years, the language industry relied on a single definition of quality: a perfect human translation. That approach worked when content volumes were small and deadlines flexible. But today’s digital environment has changed everything.
According to Mondello, companies now face an explosion of multilingual content, from internal memos to highly regulated patient-facing materials. Budgets and timelines no longer support a “perfect-every-time” model. Instead, clients now require a Fit for Purpose, risk-aware approach: the right level of quality for each content type, not the maximum level for all.
This shift has transformed how Language Scientific guides clients, prioritizing risk, intent, and downstream impact rather than static quality metrics.
Section 2: Defining Quality at Scale in Today’s AI-Driven World
Quality at scale is no longer defined by a single outcome. Instead, it requires strategic coaching, education, and clear risk assessment.
AI now enables clients to translate more content than ever while working within the same historical budgets. But Mondello emphasizes that scalable quality comes from deliberately combining:
- AI-powered translation for efficiency
- Human subject-matter expertise for accuracy
- Risk-based evaluation to determine where humans are essential
High-risk materials (e.g., clinical, pharmaceutical, surgical instructions) require full expert oversight. Low-risk content can accept small grammatical or stylistic imperfections, as long as meaning remains accurate. This is the core of the “quality spectrum” that modern life science companies must adopt.
Section 3: How AI Is Changing the Way We Measure Quality
AI has exposed the limitations of the industry’s traditional error-counting approach (e.g., counting punctuation, grammar, or stylistic errors).
As Mondello explains, error counts often fail to reflect actual risk or impact. A punctuation error in an internal memo is acceptable; in patient-facing instructions, it is not.
AI is accelerating a major shift toward:
Risk-aware quality metrics
which evaluate content based on its downstream impact, such as:
- Time to publish
- Safety outcomes
- Support burden
- Patient comprehension
- Regulatory accuracy
Dynamic QA profiles
AI is pushing LSPs to move away from one-size-fits-all rubrics (e.g., MQM) and toward tailored QA profiles aligned with content risk, audience, and use case.
ISO 11669 guidance also reinforces the concept that quality goals must match the content’s purpose—not perfection for its own sake.
Section 4: Where AI Quality Tools Work—and Where They Break Down
AI contributes significant value in two places:
1. Precision Filters
Quality estimation (QE) helps determine the initial quality level of AI output and whether light, moderate, or intensive human review is needed.
2. Productivity Amplifiers
Automatic Post-Editing (APE) can quickly correct low-risk, surface-level issues before human review, reducing manual workload.
AI still cannot arbitrate truth.
Humans remain essential because AI struggles with:
- Context-sensitive meaning
- Technical accuracy in specialized fields
- Tone and intent
- Cross-sentence cohesion
- In-document terminology consistency
- Formatting dependencies
The Risk of “False Corrections”
One of the most dangerous behaviors is when AI rewrites text to sound fluent but introduces factual inaccuracies. These “hallucinations”—especially in life sciences—can mislead even expert reviewers because of how polished and confident the incorrect output appears.
Section 5: Why Humans Are Still the Core of Life Science Translation
Far from replacing linguists, AI elevates them. Mondello emphasizes that specialized linguists are the knowledge holders who ensure factual accuracy in regulated content. The goal isn’t to remove them, it’s to remove wasted human touch by automating low-value tasks and redirecting experts toward the critical decisions AI cannot make.
Humans remain essential for:
- Validating medical and scientific accuracy
- Guarding against hallucinations
- Evaluating domain-specific nuance
- Ensuring patient and provider safety
- Overseeing model drift in LLM training
Human-in-the-loop is not optional in life science translation, it is the foundation of responsible AI.
Section 6: How Language Scientific Talks to Clients About AI, Cost, and Risk
Mondello notes that clients often arrive at one of two extremes:
- AI seems like a magic button that eliminates humans
- AI seems risky, untrustworthy, or unsafe
The key is education.
Language Scientific helps clients understand:
- Where AI safely accelerates timelines
- Where humans must intervene
- How risk mitigation protects accuracy
- How AI reduces wasted human effort—not expert insight
- How workflows adjust based on content risk
AI delivers transformational cost and time savings only when paired with deliberate safeguards, transparent quality discussions, and realistic expectations.
Section 7: What Will Drive—or Block—AI Adoption in Life Sciences
Client adoption hinges on their experience.
If LSPs apply AI carelessly and allow errors, hallucinations, or unsafe output to reach users, clients lose trust. Responsible implementation is critical for long-term adoption.
What will accelerate adoption:
- Positive, safe, repeatable outcomes
- Clear education around risks and safeguards
- Domain-specific models trained with high-quality data
- Consistent monitoring to avoid model drift
What will hinder adoption:
- Overconfidence in unsupervised AI
- Poorly trained models
- Lack of ongoing quality monitoring
- Unsafe automation in high-risk content
- Failed early experiences with AI output
Mondello predicts confidence will rise as AI technology improves—especially as quality estimation and automatic post-editing mature—and as companies see consistent results backed by strong human oversight.
Section 8: Key Takeaways from the Fireside Chat
1. Quality is no longer one standard—it’s a spectrum.
Quality now depends on risk, audience, and the purpose of the content.
2. AI scales output, but humans guarantee accuracy.
Subject-matter experts remain the truth holders.
3. Risk mitigation is the core of modern translation workflows.
AI tools must be paired with human guardrails.
4. Cost savings come from eliminating wasted human touch—not removing humans.
Efficiency improves by elevating experts, not replacing them.
5. Responsible implementation will determine the industry’s future.
Positive experiences build client confidence; careless use destroys it.
Ready to translate with medical-grade confidence?
AI can accelerate your workflow, but accuracy, safety, and regulatory consistency still require expert oversight. Language Scientific combines AI-optimized efficiency with human subject-matter expertise to deliver the right quality for every content type, every time.
Let’s talk about how to modernize your translation process safely and efficiently.
Speak with an Language Scientific Expert
