AI is rapidly reshaping how life sciences organisations work — and translation is no exception. Faced with ever-growing submission volumes, compressed timelines and global programmes that rarely run sequentially, many Regulatory Affairs teams are understandably asking the same question: can AI help us translate faster, and still submit with confidence?
The short answer is yes — but only if AI is treated as an accelerator within a controlled system, not as a replacement for regulatory-grade discipline.
A regulatory submission dossier is not a single “document”. It is an interconnected body of evidence designed to demonstrate quality, safety and efficacy through multiple lenses. A typical dossier can span manufacturing process validations, analytical method validation reports, risk assessments, Clinical Study Reports, Risk Management Plans (RMPs), CMC sections, stability data, specifications, protocols and labelling materials. Each component has a distinct purpose, yet every component must align: terminology, numbers, claims, references and the internal logic of the submission.
And the scale is significant. For new chemical entities, biologics and combination products, it is entirely normal for dossiers to exceed 1,000 pages. Add regulatory windows that cannot slip, parallel workstreams across regions, and the reality that “late-breaking” updates are often unavoidable. In that environment, AI becomes attractive not because it is fashionable, but because it seems to offer a path to delivery: translate more content, more quickly, with fewer bottlenecks.
The challenge is that regulatory documentation punishes plausible errors. Which brings us to the most important risk RA teams need to understand when AI enters the translation workflow: hallucinations.
Hallucinations: when “plausible” becomes dangerous
In the context of generative translation — where machine translation is combined with large language models — a hallucination occurs when the system produces content that reads convincingly but is not supported by the source. It may introduce details that were never present, subtly alter meaning, or “helpfully” resolve ambiguity by guessing. In day-to-day communications, this might be an inconvenience. In a submission, it can be a critical failure.
A simple example illustrates the risk. An AI tool may “invent” a trial approval date, adjust a dose, or translate a numerical value incorrectly (e.g., 10 mg becoming 100 mg). Sometimes the error is obvious; often it is not. The more expert and polished the output sounds, the easier it is for a flawed statement to pass through internal review — particularly under time pressure.
Why does it happen? Typically, for three reasons:
- Context gaps: regulatory text is dense, cross-referential and often written in a way that assumes deep domain knowledge.
- Domain limitations: unless systems are tuned with validated terminology and constraints, they will default to general patterns rather than regulatory conventions.
- Source ambiguity: where the original is unclear, models can “complete” the idea rather than preserve the uncertainty.
The insight for Regulatory Affairs is this: AI errors are not always translation errors in the traditional sense. They can be content integrity errors. That changes how risk must be managed.
A practical mindset: control, not avoidance
The debate should not be “AI: yes or no”. It should be “where does AI add value, and what controls make it safe enough for regulated content?”
If AI is deployed with the same discipline applied to data integrity, document control and publishing validation, it can materially improve throughput. But if it is used informally — or without clear boundaries — it introduces a new and often invisible failure mode.
What does “controlled AI” look like in practice?
1) Human validation is non-negotiable — and must be purposeful
AI-assisted output should always be validated by specialists in pharmaceutical translation and regulated content. This is not a cosmetic edit. Reviewers must actively interrogate:
- numerical accuracy (doses, concentrations, statistics)
- terminology consistency across modules
- preservation of meaning and scientific intent
- alignment with the regulatory context (FDA/EMA/other expectations)
Most importantly, the review must include an explicit “hallucination check”: a deliberate search for content that wasn’t in the source, or that has been subtly reinterpreted.
Segment-by-segment comparison against the source text remains one of the most reliable defences. Under time pressure, it is also the first control that teams are tempted to relax — and often the one that matters most.
2) Prompting is not a trick — it is governance
The quality of AI output is heavily influenced by how the system is instructed. In a regulatory setting, prompts should act like procedural controls:
- define the document type (CSR, validation protocol, risk assessment, labelling)
- require exact reproduction of numbers and units
- prohibit interpretation, summarisation or “improvements”
- enforce the use of validated glossaries and approved terminology
- instruct the model to flag uncertainty rather than resolve it
The objective is not to coax the model into sounding better. It is to constrain the model into behaving predictably.
3) QA must shift left — and run continuously
In regulatory translation, QA cannot be a final-stage checkbox. It needs to run throughout the process and focus on the risk drivers regulators care about:
- consistency of product, substance and strength naming
- numerical and unit fidelity
- cross-reference integrity
- structural compliance for publishing formats (where applicable)
- coherence between related sections across the dossier
A late-stage QA pass will catch formatting issues. It may not catch a believable “fabricated” statement buried inside a narrative.
4) Define boundaries: what AI may do — and what it must never do
AI can be highly effective for first-draft production, repetitive content and controlled reuse — especially where strong translation memory and terminology assets already exist.
Where it should be treated with heightened caution is in content that is inherently high-risk:
- dosing, administration and safety statements
- risk assessments and mitigation language
- conclusions drawn from study results
- critical CMC validations
- labelling and patient-facing content
The principle is simple: the closer the text sits to patient safety, regulatory decision-making, or compliance-critical claims, the stronger the controls must be.
5) Auditability and traceability are part of “regulatory readiness”
If AI is used in dossier translation, teams should be able to demonstrate control: what tool was used, under what constraints, what was reviewed, and how changes were approved.
Version control, review logs, and a clear record of the validation pathway are not administrative overhead. They are part of making the translation process defensible —internally, and if ever questioned.
How we approach this at Lexic
At Lexic, we use advanced language technology —including AI-assisted workflows, translation memories and specialised glossaries— to deliver speed and consistency at scale.
But we do not confuse technology with assurance.
Regulatory submission translation ultimately rests on human accountability. That is why we prioritise domain expertise and regulatory know-how above all else. Every AI-assisted output is validated by specialist linguists with pharmaceutical and regulated-content experience, supported by structured QA and full traceability. Technology accelerates the process; expert oversight protects its integrity.
Conclusion: speed is useful — controlled speed is strategic
AI offers significant opportunities to improve efficiency in the translation of regulatory dossiers. Yet it also introduces tangible risks — particularly hallucinations and critical inaccuracies that may compromise submission integrity.
For Regulatory Affairs professionals, the objective is not merely speed, but controlled speed. An effective approach combines AI capabilities with rigorous human validation, clearly defined usage boundaries, structured QA and full traceability.
By adopting a hybrid, expert-led model, organisations can benefit from technological innovation while maintaining the precision, coherence and compliance required in regulatory submissions.
At Lexic, we harness advanced technology — but we always place human expertise at the centre of the process. That is how we ensure high-quality translations and minimise the risk of critical errors or hallucinations in regulatory dossiers.
