Clinical notes. Imaging reports. Lab results. Transcripts from patient conversations. It’s all full of critical insights, but none of it fits neatly into rows and columns – it’s all unstructured data. It’s sensitive. It’s messy. And it’s growing by 137 terabytes a day.
When it comes to activating the value of unstructured health data, you can’t rely on a one-size-fits-all model. You need systems that understand context, nuance, and ambiguity. That’s where our linguistic team comes in.
Our linguistics-first approach means our technology is designed not just to read language but to understand it. Our data team includes experts in semantics, pragmatics, sociolinguistics, morphology, and syntax. These are people who spend their careers decoding how humans actually use language, including all the ways that meaning can shift based on context, structure, or even dialect.
When we look at healthcare specifically, it’s like medical data has its own dialect, with shorthand, acronyms, telegraphic sentences, jargon… And to be able to tap into this dialect, we need to understand its ambiguity, context and variety.
The role of linguistics in AI training
There’s a big difference between AI that’s simply trained on language data and AI that’s built with linguistic insight.
Most big tech companies train their AI models by feeding large volumes of data into powerful algorithms to extract patterns. But when we are dealing with health data: It’s filled with nuance, ambiguity, and variation. It’s not just about what is said—it’s about how and why. Traditional models simply don’t cut it: we need an out-of-the-box approach. We need to break some rules.
That’s why we take a different path.
At Private AI, instead of just feeding data into a model and hoping for the best, our language experts stay involved at every step. They help choose the right, real-world examples to teach the AI, including misspellings and messy abbreviations. They guide how the information is labelled–if a certain term refers to a condition, to a process, a treatment, or a drug. They verify what the AI gets right (and wrong) and figure out where it needs more help, for example, differentiating between a name and a condition. Their job is to make sure the AI really understands how people use language, especially in complex medical situations.
They don’t just label words. They understand why certain errors happen and how to correct and prevent them—whether it’s a missed coreference, a misclassified entity, or a subtle shift in meaning due to context.
Why Linguistics Matters in Healthcare
Healthcare data isn’t just complex, it’s human. The same condition might be described in five different ways depending on the doctor, the country, or the context. This is where many AI tools fail. They strip out sensitive information, but also strip out meaning. Or they leave in personally identifiable information because they don’t recognize it in the first place.
Our linguistics-first approach is built for this reality. It’s how we maintain the clinical context and ensure that the AI understands the difference between a diagnosis, a name, and a treatment. And it’s how we can transform unstructured data into usable, AI-ready assets—without compromising patient privacy or data quality.
Let’s take a step back and look at what makes health data so tricky for AI. It’s full of ambiguity.
A word like “Graves” could be a disease, a surname, or something else entirely. “Pain” might refer to a symptom or a person’s family name. Traditional, pattern-based AI systems can struggle with these edge cases because they don’t understand context. But linguists do.
Our linguistics-first approach is built to navigate exactly this kind of complexity. It’s designed to spot and make sense of the things that trip up conventional models, like:
- • Overlapping terms: Recognizing whether a word is a symptom, a condition, or a person’s name (think “Raynaud,” “Crohn,” or “Hodgkin”).
- • Clinical shorthand and jargon: Understanding that “R/o PE” means “rule out pulmonary embolism,” not random letters.
- • Messy transcripts: Dealing with real-world doctor-patient conversations where speech is fragmented, interrupted, or unclear.
- • Different date formats: Telling the difference between “03/07/22” meaning March or July depending on where you are, and redacting the right parts accordingly.
- • Identifying people: Knowing when a name refers to the patient and needs redaction, versus when it’s a clinician that you might want to keep as is.
You can’t solve these challenges with brute-force data or keyword matching. You need systems that understand intent, structure, and relationships. And for that, you need linguists. Our linguists don’t just annotate data, they:
- • Curate high-quality, representative datasets
- • Design annotation guidelines rooted in linguistic theory
- • Conduct meticulous error analysis to refine model performance
- • Research, develop, and test new features like coreference resolution and relation extraction
In short, they make sure our technology reflects how real people use language.
Critical health data drives decisions that affect people’s lives. And with so much of it trapped in unstructured formats, we’re leaving life-saving insights on the table.
Linguistics makes those insights accessible. It enables:
- • Faster diagnoses, by pulling meaning from physician notes
- • Smarter clinical trials, by connecting scattered patient data
- • More personalized care, by capturing nuance and context in patient-reported outcomes
- • Real-world evidence generation, by organizing data from wearables, chatbots, and beyond
When you pair linguistic precision with AI’s scale, you get systems that don’t just process data—they make it usable.
At Private AI, we believe you can’t transform health data without deeply understanding language. That’s why we’ve built a team of linguists who do more than annotate: they engineer the intelligence behind our solutions.
Because AI is only as good as the people who shape it. And when you need to find information within complex data, linguists aren’t just helpful. They’re essential.
