AI Translation in Biopharma: Why Human Regulatory Review Remains Essential

Release date：2026-01-15

As biopharmaceutical companies accelerate their global expansion, an increasingly underestimated risk is coming into focus: clinical or regulatory submission documents being flagged by authorities such as the EMA or FDA for translation-related issues.

In many cases, the problem does not lie in the data itself, but in how key documents—such as the Investigator’s Brochure (IB), Clinical Study Report (CSR), or Common Technical Document (CTD)—are worded. Terminology and phrasing that appear linguistically correct may still fail to align with regulatory expectations.

This highlights a fundamental reality in highly regulated environments: linguistic accuracy does not equal regulatory acceptability. Human regulatory review remains indispensable.

I. Three Major Compliance Blind Spots of AI Translation in Biopharma

1. “Accurate” Terminology ≠ “Regulatorily Acceptable” Language

Medical terminology is governed not only by meaning, but by regulatory context.

Efficacy vs. Effectiveness

In confirmatory clinical trials, regulators expect evidence demonstrating treatment effects under controlled conditions (efficacy). Real-world evidence (RWE), by contrast, plays a supplementary role in regulatory decision-making.

The FDA (Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products; Considerations for the Use of Real-World Data and Real-World Evidence…) has clearly articulated expectations for substantial evidence of effectiveness in its guidance on human drugs and biological products, while separately defining the appropriate scope of real-world data and evidence in regulatory use.

As a result, using effectiveness in a document centered on confirmatory evidence may raise concerns regarding study rigor or evidence hierarchy.

Adverse Event (AE) vs. Serious Adverse Event (SAE)

Clinical documentation must strictly differentiate between AE and SAE definitions and reporting requirements. Failure to do so compromises traceability and compliance of safety data.

These distinctions are mandated by harmonized clinical safety standards established by the FDA and ICH (e.g., FDA’s E2A Clinical Safety Data Management: Definitions and Standards for Expedited Reporting; ICH E2A), not stylistic language preferences.

Sponsor Responsibilities

The definition of sponsor obligations varies by jurisdiction and is grounded in specific regulatory frameworks.

In the EU, responsibilities must align with ICH GCP principles; in the United States, sponsor duties must conform to FDA IND submission requirements (e.g., ICH E6(R3); 21 CFR 312.23).

Terminology that deviates from these frameworks may trigger compliance risks during regulatory review.

👉 Key limitation:

AI can select words that appear semantically reasonable, but it cannot determine which formulation aligns with established FDA, EMA, or ICH regulatory consensus. Compliance is not about language correctness—it is about regulatory alignment.

2. Fluent Language ≠ Clinically Executable Instructions

In documents such as IBs, CSRs, or clinical protocols, the primary objective of language is to eliminate execution ambiguity, not to improve readability.

AI systems often attempt to “optimize” text by:

🔹Merging repeated exclusion criteria

🔹Simplifying complex sentence structures

For example, rewriting “Subjects must discontinue anticoagulant therapy at least 24 hours prior to screening” as “Screening occurs 24 hours after discontinuation of anticoagulants” Such changes may appear minor, but they can weaken critical risk controls.

The latest ICH GCP guidance explicitly emphasizes that clinical protocols must be clear, unambiguous, and sufficient to ensure consistent implementation across study sites (ICH E6(R3)).

3. Lack of Risk-Based Prioritization

Not all content in biopharmaceutical documentation carries the same regulatory risk.

High-Risk Content	Lower-Risk Content
Indication descriptions, safety statements	Institutional names, addresses
Inclusion/exclusion criteria	General methodology descriptions
Sponsor responsibility clauses	Instrument model numbers

Errors in high-risk sections can have consequences far beyond technical inaccuracies. Current AI systems cannot identify which sentences must be preserved verbatim and which allow linguistic flexibility. Compliance-focused translation is fundamentally a risk-based professional judgment process.

II. AI Translation vs. Human Regulatory Review: Key Differences

Dimension	AI Translation	Human Regulatory Review
Core Logic	Probabilistic semantic matching	Alignment with regulatory review practices
Terminology Handling	Corpus-based statistical output	Dynamic selection based on FDA/EMA/ICH guidance
Risk Awareness	No risk stratification	Identifies and prioritizes high-risk content
Regulatory Adaptation	Generic output	Tailored to target authority expectations
Output Standard	Fluent and grammatically correct	Compliant with ICH structure and review conventions

Example: A CSR must fully comply with ICH E3 structural requirements. Human reviewers ensure 100% structural compliance, whereas AI can only guarantee grammatical accuracy.

💡Landelion Insight:

Human review is not error correction—it is pre-emptive compliance decision-making. Based on historical regulatory interactions (e.g., repeated EMA queries triggered solely by terminology issues), expert reviewers proactively adjust language to shift compliance from reactive remediation to proactive risk prevention.

III. Why “AI + Human Review” Has Become the Industry Standard

In the regulatory landscape of global biopharma, AI–human collaboration is not optional—it is a necessity.

First, AI must be positioned as an efficiency lever, not a compliance authority. For large-volume documents such as CSRs or QMS materials, AI can significantly accelerate draft generation and terminology consistency. However, regulatory submission readiness depends on human review informed by regulatory precedent.

Second, human reviewers bring deep regulatory-context expertise, typically combining:

🔹Academic training in pharmacology or clinical medicine

🔹More than five years of IND/CTD submission experience

🔹Continuous tracking of FDA, EMA, and PMDA guideline updates

This enables precise judgment, such as whether a statement on patient adherence aligns with EMA review expectations under the ICH E8 framework—an assessment beyond AI capability.

Most importantly, this hybrid model is now widely adopted across multinational pharmaceutical companies and innovative biotech firms. As AI matures in efficiency, organizations increasingly reserve it for standardized, low-risk drafting, while relying on regulatory-trained human experts for pre-submission review to mitigate rework risk.

Conclusion: Biopharma Translation Is Regulatory Decision Support

Global biopharmaceutical expansion is not merely market entry—it is a complex undertaking spanning science, regulation, and culture. AI technology delivers unprecedented efficiency gains, but when patient safety, legal responsibility, and market authorization are at stake, machines cannot replace human judgment grounded in regulatory context.

The sustainable path forward is not “fully manual” or “fully automated,” but a balanced model: AI drives efficiency; humans ensure compliance. This is not just a translation strategy—it is respect for global regulatory systems.