Medical accuracy in the age of AI: Why context remains critical

Artificial intelligence has transformed how medical content is translated. AI-driven tools offer speed, affordability, and scalability — making it possible to adapt curricula, e-learning modules, and assessments for international learners at a pace that would have been unthinkable a decade ago.

For universities, medical schools, and training providers, this can support rapid programme launches, expanded international enrolment, and more inclusive access to education.

But AI still has a fundamental limitation: it lacks human context.

The risk of translating isolated terms 

Even with sophisticated algorithms, AI often struggles with context. In medical education, a single mistranslation can cause confusion for learners or jeopardise accreditation.

Consider the following examples:

• In a physiology module, the term “lead” referred to an ECG lead. AI rendered it instead as the verb “to lead.”

• In a pharmacology exam, the word “control” was meant to describe a control group. AI translated it as a verb (“to control”), altering the meaning of the entire question.

• In a nursing case study, “discharge” was intended as patient discharge, but AI interpreted it as electrical discharge.

On their own, these may look like minor slips. But in an assessment or training context, such errors can undermine student comprehension, skew exam results, and risk accreditation reviews.

Why context drives quality in education 

Curricula, e-learning platforms, and assessments often contain short strings, isolated questions, or case-based prompts. When these are translated without context, the meaning is easily distorted.

Providing translators (and AI engines) with contextual information — such as course objectives, metadata, usage notes, or screenshots from the LMS — can dramatically improve accuracy. Context ensures that a pharmacology exam tests the right concept, that a case study mirrors clinical reality, and that learning outcomes remain consistent across languages.

Proactive steps to avoid translation errors 

At Novalins, we’ve developed workflows that help education teams maintain accuracy and accreditation standards when translating medical content:

• Collect learning objectives, curricula maps, or exam blueprints alongside text.

• Provide slides, case studies, or LMS screenshots to show how content will appear.

• Use subject-matter expert reviewers with teaching backgrounds to validate terminology.

• Encourage early discussion of unclear terms instead of leaving them to assumption.

• Establish a glossary of key concepts aligned with faculty-approved terminology.

• Involve programme directors or coordinators as reviewers within the process.

These steps keep translation aligned with both academic integrity and learner needs.

Collaboration: The foundation of translation quality 

Whether AI is used to pre-translate, or content is fully managed by human linguists, one truth remains: context must guide the process.

The most successful outcomes come from collaboration — pairing automation with expert review, and pairing text with the broader story it supports.

In medical education, every word influences learner understanding, assessment fairness, and accreditation outcomes. Context is not optional — it’s essential.

So when preparing content for translation, don’t just send the strings. Send the story behind them.

References 

• Genovese A, Borna S, Gomez-Cabello CA, Haider SA, Prabha S, Forte AJ, Veenstra BR. Artificial intelligence in clinical settings: a systematic review of its role in language translation and interpretation. Ann Transl Med. 2024 Dec 24;12(6):117. doi: 10.21037/atm-24-162. Epub 2024 Dec 17. PMID: 39817236; PMCID: PMC11729812. 

• Delfani, J., Orasan, C., Saadany, H., Temizoz, O., Taylor-Stilgoe, E., Kanojia, D., Braun, S., & Schouten, B. (2024). Google Translate error analysis for mental healthcare information: Evaluating accuracy, comprehensibility, and implications for multilingual healthcare communication (arXiv:2402.04023). arXiv. https://doi.org/10.48550/arXiv.2402.04023