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

Artificial intelligence has revolutionized the field of medical translation. AI-driven tools deliver unmatched speed, affordability, and scalability. For global pharmaceutical and MedTech companies, this translates into faster product launches, streamlined localization processes, and the ability to handle content in dozens of languages all at once. 

But even with its growing power, AI has a fundamental limitation: it lacks human context. 

The risk of translating isolated terms 

Despite its advanced algorithms, AI-based medical translation often struggles with context. 

Healthcare language is about more than just words — it’s about meaning, tone, and cultural understanding. A single mistranslation can lead to confusion or even compromise safety. AI, no matter how well-trained, may fail to distinguish homonyms, grasp intent, or identify subtle cues without adequate context. 

Over the past year, we’ve had several clients bring us AI-generated translations for medical apps. In each case, the content consisted of disconnected strings — interface labels, button text, or error messages — usually extracted from spreadsheets or codebases, completely detached from their intended usage. 

These may seem like simple translation tasks at first. But deeper review often reveals serious issues. 

Take the word “lead.” In one cardiology app, it referred to ECG leads. But the AI interpreted it as either the verb “to lead” or as a leadership term — both grammatically correct but completely wrong in context. 

Or consider “control.” In clinical settings, it often refers to a control group. But when the AI saw the term alone, it rendered it as a verb (“to control”) or as a generic UI element — leading to inaccurate and potentially misleading translations. 

Why context drives quality in AI-assisted medical translation 

In apps, digital tools, and health platforms, short strings are common — but translating them without context is risky. In medicine, even a brief phrase can carry critical meaning. Without guidance, even the most advanced AI (or a skilled human translator) can misinterpret the intent. 

It’s easy to assume that source files are enough. But translation quality hinges on more than text. Metadata, usage notes, tone of voice, screenshots, and reference materials all play a key role in ensuring accuracy and relevance. 

Proactive steps to avoid translation errors 

At Novalins, we’ve refined our processes to ensure clarity and context from the beginning: 

• We ask for contextual information for all standalone strings 

• We support tools that provide string tagging, screenshots, or in-code developer notes 

• Our medical linguists are trained to flag unclear terms early, encouraging discussion instead of assumption 

• We collaborate on glossary creation and approval before the project begins 

• We collect reference materials — such as previous translations or mockups — to understand the content’s function 

• We recommend involving an internal stakeholder who can provide feedback in real-time within our TMS 

• Most importantly, we maintain continuous communication between our project team and the client to ensure consistency from start to finish 

Collaboration: The foundation of translation quality 

Whether you’re using AI to pre-translate or relying entirely on human experts, the message is clear: context must be provided. A knowledgeable language service provider can help structure your content, optimize for localization, and ensure that your app or digital health tool is safe, accurate, and regulatory-compliant. 

AI brings real benefits in terms of cost and speed — but it also brings new risks. The best outcomes come from collaboration: pairing automation with expert review, and pairing content with clear context. 

In digital health, where every word can influence patient care or regulatory compliance, context isn’t a luxury — it’s essential. 

So when preparing content, don’t just send the strings. Send the story that goes with 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