Precisión médica en la era de la IA: Por qué el contexto sigue siendo fundamental 

En el mundo de la traducción médica impulsada por inteligencia artificial, el auge de la IA ha sido verdaderamente revolucionario. Las herramientas de traducción automática ofrecen hoy una velocidad, eficiencia de costes y escalabilidad sin precedentes. Para las empresas farmacéuticas y de tecnología médica globales, esto significa una salida al mercado más rápida, flujos de localización más fluidos y la capacidad de gestionar docenas de idiomas al mismo tiempo. 

Pero por poderosa que sea la IA, tiene un punto ciego importante: el contexto. 

El peligro oculto de las cadenas aisladas 

A pesar de sus capacidades, la traducción automática en el ámbito sanitario presenta una limitación crítica: a menudo carece de comprensión contextual. 

La comunicación médica no trata solo de palabras. Se trata de significado, matices y claridad cultural. Incluso un solo término mal ubicado puede tener consecuencias reales en un entorno de atención médica. La IA, por más entrenamiento lingüístico que tenga, a menudo no puede distinguir entre homónimos, comprender el tono o interpretar la intención sin un contexto claro. 

Durante el último año, varios clientes se han acercado a nosotros con contenidos generados por motores de traducción automática para sus aplicaciones médicas. En cada caso, el contenido original estaba compuesto por cadenas aisladas —términos de interfaz, etiquetas, botones y mensajes de error—, a menudo almacenados en hojas de cálculo o repositorios de código, completamente desconectados de su uso real. 

A primera vista, traducir estas cadenas parece una tarea rápida. Pero pronto identificamos problemas importantes. 

Tomemos la palabra “lead”. En una app, se usaba en el sentido cardiológico (derivaciones de ECG). Pero la IA, sin conocer esto, la tradujo como el verbo “liderar” o el sustantivo relacionado con el liderazgo. ¿El resultado? Gramaticalmente correcto, pero completamente erróneo —y potencialmente confuso en un contexto médico. 

Otro problema recurrente: el término “control”. En investigación clínica, puede referirse a un grupo de control. Pero cuando se presenta solo, la IA lo traduce como un verbo (“controlar”) o como un elemento de interfaz (como un botón), según su entrenamiento. Sin pistas contextuales, la precisión se desmorona. 

Por qué el contexto es esencial en la traducción médica asistida por IA 

En interfaces de usuario, aplicaciones para pacientes y herramientas digitales de salud, las cadenas aisladas son la norma —pero eso no significa que puedan traducirse a ciegas. Especialmente en salud, incluso una frase corta puede tener un significado crítico. Sin contexto adicional, incluso la IA mejor entrenada (¡y hasta un lingüista humano!) puede cometer errores. 

Los clientes suelen asumir que al proporcionar solo los archivos fuente, el trabajo está casi terminado. Pero en realidad, la calidad del resultado depende en gran medida de la entrada: metadatos, notas de uso, capturas de pantalla, archivos de referencia y aclaraciones sobre el tono o el público hacen una gran diferencia. 

¿Cómo prevenir estos errores? 

En Novalins, hemos creado procesos para afrontar este desafío: 

• Solicitamos notas contextuales para cadenas aisladas. 

• Fomentamos el uso de herramientas que permiten etiquetar cadenas, añadir capturas de pantalla o comentarios de desarrolladores. 

• Nuestros lingüistas médicos están entrenados para detectar ambigüedades desde el principio y fomentar el diálogo en lugar de asumir. 

• Recomendamos crear y aprobar glosarios con el cliente antes de comenzar el proyecto. 

• Pedimos material de referencia, como traducciones anteriores o maquetas de diseño, para entender mejor la función del contenido. 

• Sugerimos involucrar a alguien del equipo interno del cliente que pueda ofrecer comentarios en tiempo real dentro de nuestro sistema de gestión de traducciones (TMS), para resolver problemas durante el proyecto —no solo después de la entrega. 

• Y lo más importante, enfatizamos la comunicación constante entre el cliente y nuestro equipo de proyecto para garantizar una alineación completa de principio a fin. 

La colaboración hace posible la calidad 

Ya sea que uses IA para pretraducir contenido o confíes en un equipo humano desde el inicio, la conclusión es clara: el contexto debe compartirse. Un proveedor de servicios lingüísticos (LSP) capacitado puede guiar el proceso, optimizar la documentación de cadenas y garantizar que el contenido de tu aplicación sea seguro, preciso y conforme. 

Cuando se trabaja con traducción automática, la velocidad y el ahorro de costos son reales. Pero también lo son los riesgos. Los mejores resultados provienen de la colaboración —combinando automatización con revisión experta, y contexto rico con juicio lingüístico. 

En el mundo vertiginoso de la salud digital, donde cada palabra puede afectar una decisión del usuario o un resultado regulatorio, el contexto no es solo útil: es fundamental. 

Así que si estás preparando contenido para una app, no envíes solo las cadenas. Envía la historia detrás de ellas. 

Referencias 

1. 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. 

2. 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 

Medical accuracy in the age of AI: Why context still reigns supreme 

In the world of AI-powered medical translation, the rise of artificial intelligence has been nothing short of revolutionary. AI-powered translation tools now offer unprecedented speed, cost-efficiency, and scalability. For global pharmaceutical and MedTech companies, this means faster time-to-market, smoother localisation workflows, and the ability to manage dozens of languages simultaneously. 

But as powerful as AI is, it has one significant blind spot: context. 

The hidden danger of isolated strings 

Despite its capabilities, machine translation for healthcare has a critical limitation: it often lacks contextual understanding.  

Medical communication is not just about words. It’s about meaning, nuance, and cultural clarity. Even a single misplaced term can have significant consequences in a healthcare setting. AI, for all its linguistic training, often lacks the ability to distinguish between homonyms, understand tone, or interpret intent without clear context. 

Over the past year, we’ve had multiple clients approach us with content generated by AI translation engines for their medical apps. In each case, the source content was composed of isolated strings — interface terms, labels, buttons, and error messages — often stored in spreadsheets or code repositories, completely detached from their real-world usage. 

At first glance, translating these strings seems like a quick task. But we quickly identified major issues. 

Take the word “lead”. In one app, it was used in the cardiology sense (ECG leads). But AI, unaware of this, translated it as the verb “to lead” or the noun referring to leadership. The result? Grammatically correct, but completely wrong — and potentially confusing in a medical context. 

Another recurring issue: the term “control”. In clinical research, this might refer to a control group. But when presented alone, the AI rendered it as a verb (“to control”) or as a UI element (like a button), depending on its training. Without contextual cues, accuracy fell apart. 

Why context is essential in AI-assisted medical translation 

In user interfaces, patient apps, and digital health tools, isolated strings are the norm — but that doesn’t mean they can be translated blindly. Especially in healthcare, even a short phrase can carry critical meaning. Without additional context, even the best-trained AI (and even a human linguist!) can make the wrong call. 

Clients often assume that by providing source files alone, the job is 90% done. But in reality, the quality of the output depends largely on the input: metadata, usage notes, screenshots, reference files, and clarification about tone or audience all make a massive difference. 

How to prevent these errors? 

At Novalins, we’ve built processes to handle this challenge: 

• We request contextual notes for isolated strings. 

• We encourage the use of tools that allow string tagging, screenshots, or developer comments. 

• Our medical linguists are trained to flag ambiguities early in the process, prompting dialogue instead of assumptions. 

• We recommend creating and approving glossaries with the client before the project begins. 

• We ask for reference material, such as previous translations or design mockups, to better understand the content’s function. 

• We suggest involving someone from the client’s internal team who can provide live feedback during the project, directly within our TMS, to resolve issues in real time — not just after delivery. 

• And most importantly, we emphasize constant communication between the client and our project team to ensure full alignment from start to finish. 

Collaboration makes quality possible 

Whether you’re using AI to pre-translate content or relying on a human team from the start, the takeaway is clear: context must be shared. A skilled LSP can help guide the process, optimize string documentation, and ensure that your app content is safe, accurate, and compliant. 

When working with AI translation, speed and cost savings are real. But so are the risks. The best results come from collaboration — combining automation with expert review, and rich context with linguistic judgment. 

In the fast-paced world of digital health, where every word can impact a user decision or a regulatory outcome, context isn’t just helpful — it’s critical. 

So if you’re preparing app content, don’t just send the strings. Send the story behind them. 

References 

1.  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. 

2. 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 

From human to AI to multilingual content validation: the evolution of the role of medical translation agencies 

The language services industry is undergoing a rapid transformation. Just a few years ago, most companies relied exclusively on human translation to ensure accuracy, nuance, and cultural relevance. Then came the rise of machine translation (MT), which brought dramatic gains in speed and scale—but also raised new challenges around quality and reliability. 

Today, we are witnessing the next stage in this evolution: the integration of artificial intelligence (AI) translation tools, and a growing shift toward multilingual content validation. This shift is especially visible in the medical and healthcare sectors, where accuracy is non-negotiable and new content needs to be rolled out fast, across dozens of languages. 

As artificial intelligence (AI) becomes central to translation workflows, companies are rethinking how they ensure quality—especially in the context of high-risk content.  

The rise of AI and in-house translation workflows 

More and more tech companies are internalizing their translation processes by leveraging AI tools. This gives them significant advantages: 

• Faster turnaround times, 
• Reduced costs, 
• Greater control over their multilingual content pipelines. 

However, AI translation—no matter how advanced—still lacks the contextual understanding and domain-specific expertise that is especially critical for healthcare translation accuracy in regulated industries like healthcare. 

To compensate for this, companies are building hybrid workflows:
AI translation is followed by in-house post-editing, and then by external validation from specialised medical linguists. This new model signals a shift in how companies view language service providers—not just as executors, but as expert validators and consultants. 

An example from our own experience: a wellness app with a new approach 

A recent case we encountered perfectly illustrates this shift. A large tech company preparing to launch a wellness app reached out to us. They needed to translate the app content into a wide range of languages to support a global rollout. 

Instead of sending their source files to a language service provider (LSP) as they might have in the past, they chose to do things differently. They used AI internally to generate the translations and handled the post-editing themselves. But when it came to validating the medical terminology and ensuring consistency with health-related standards, they turned to us—a specialized medical translation provider. 

Their goal was to ensure that the final content would be safe, compliant, and accurate, especially in terms of the medical guidance and terminology used throughout the app. Our role shifted from traditional translation to a more strategic one: validating their content and advising them on the linguistic quality control process. 

Why this matters: a glimpse into the future of medical translation 

This case is not an exception—it’s a glimpse into the future of medical translation. 

We expect to see more companies adopting this hybrid model: using AI and internal teams for speed and scale, while relying on external experts for quality assurance and validation, particularly for high-risk or specialised content. 

Why AI alone isn’t enough in healthcare translation becomes especially clear in this context. Multilingual content validation is the cornerstone of trust. It’s no longer just about getting words into another language—it’s about ensuring that those words convey the right meaning, in the right context, with full accuracy. Especially in healthcare, there’s no margin for error. 

Conclusion: quality needs collaboration 

If a company has the capability to manage machine translation and post-editing internally, that’s absolutely fine. But when it comes to medical content, relying solely on internal processes is risky. 

Specialised validation by expert medical translators is essential to ensure that the final content is not only safe, accurate, and compliant with medical standards, but also culturally appropriate. In the medical field, where communication can directly impact patient safety and treatment outcomes, it’s crucial to work with professionals who combine deep scientific and medical knowledge with a strong awareness of cultural nuances. Medical translators bring not just linguistic accuracy, but the ability to adapt terminology, tone, and context to local expectations — ensuring clarity, trust, and compliance across languages and cultures. 

What’s more, a trusted LSP can act as a strategic consultant, advising internal teams on the necessary quality steps to reach the highest possible output. 

Looking ahead, future trends in AI and medical language services point toward deeper integration of AI-driven tools with expert human validation. Automation may improve speed and scalability, but true quality still depends on the expertise of certified professionals who understand both the language and the science behind it. 

In the fast-paced world of multilingual communication, LSPs are no longer just translation providers—they are partners in quality, guardians of accuracy, and key players in the future of global healthcare communication. 

At Novalins, we support companies at every stage of this evolving process. As validation partners, we help ensure that medical translations are accurate, consistent, and aligned with the highest standards—because in healthcare, quality is never optional. 

References 

1. Noll R, Frischen LS, Boeker M, Storf H, Schaaf J. Machine translation of standardised medical terminology using natural language processing: A scoping review. N Biotechnol. 2023 Nov 25;77:120-129. doi: 10.1016/j.nbt.2023.08.004. Epub 2023 Aug 29. PMID: 37652265.