- Clinical context is essential for trustworthy, explainable AI in healthcare
- General-purpose models struggle to interpret real-world physician language
- IMO Health’s knowledge layer grounds AI in domain-specific semantic clarity
- Healthcare AI success depends on accurate, normalized clinical data
- Embedding clinical knowledge reduces risk and accelerates model deployment
The healthcare industry is all-in on artificial intelligence (AI), pouring millions into AI initiatives, particularly large language models (LLMs), to accelerate insights, improve care, and unlock new operational efficiencies.
But there’s a growing disconnect between ambition and impact.
Too often, AI models are not trained on a corpus of clinical data, leading to a lack of a solid foundation in clinical context. The result? Mistranslations, hallucinations, poor performance, and a trust gap that stalls adoption, or worse, leads to regulatory and financial risk.
The hidden knowledge gap in medical AI
Clinical documentation is incredibly nuanced. It’s shaped not just by patient complexity, but by individual physician preferences, specialty-specific conventions, and ever-evolving medical language. What’s more, the same term can mean very different things depending on who’s using it and why.
Take “PAD.” To a cardiologist, that likely refers to peripheral artery disease. To an orthopedist, it could mean something entirely different, like a protective device. Without domain-specific knowledge, an AI model can easily misinterpret and act on the wrong intent.
Most general-purpose AI tools lack:
- A longitudinal understanding of how terminology evolves in medicine
- Contextual awareness of who is documenting and in what setting
- Mechanisms for managing synonym variability and concept overlap
This missing layer of clinical context makes it nearly impossible for AI to produce reliable, physician-trusted outputs. When models fail, the investment behind them – often in the tens of millions – is jeopardized.
Building smarter AI with IMO Health’s knowledge layer
For more than 30 years, we have specialized in interpreting and structuring clinical documentation — the complex and inconsistent language physicians use every day. That deep expertise now powers a knowledge layer designed specifically for healthcare AI.
In addition to training on a large, anonymized clinical dataset representing over 12 million U.S. patients, IMO Health’s platform is continuously informed by:
- Terminology usage logs – Over 1 billion per month since 2013, totaling 18+ billion entries across real-world physician workflows
- Terminology ingestion and harmonization – 350 million+ entries per month, mapped and normalized across systems
- Editorial policies – Built from trusted medical sources, including societal guidelines for diseases, procedures, medications, and labs
Combined with our semantic models, these data streams give our knowledge layer unmatched accuracy, relevance, and clinical credibility.
Our platform provides:
- Semantic clarity across specialties – factoring in variables like age, gender, diagnosis history, and provider type
- Continuously updated mappings and ontologies that reflect real-world clinical usage, including new terminology, synonyms, and code sets
- Context-aware normalization that understands not just what was said, but what was meant
This is not just about improving natural language processing (NLP) accuracy. It’s about grounding AI in the clinical truth of your organization — so outputs are trustworthy, explainable, and able to be used confidently in your downstream applications.
Why healthcare AI needs IMO Health to work
AI initiatives don’t operate in a vacuum. They live within complex systems of data pipelines, documentation workflows, and regulatory scrutiny. Without a layer that harmonizes terminology at the source, many AI models face issues like:
- Reduced model explainability due to inconsistent or ambiguous inputs
- Increased risk of false positives or incorrect recommendations
- Delayed deployment due to time-consuming manual data mapping
You can streamline development and deliver accurate and actionable outputs by embedding IMO Health’s clinical knowledge layer into your AI architecture.
That’s how health tech organizations use IMO Health’s supporting technology to:
- Mitigate risk by aligning outputs with clinical reality
- Accelerate time to value by reducing the need for custom preprocessing
- Build physician and stakeholder trust by enabling greater explainability — understanding the why behind model outputs
As we explored in our guide to AI-powered medical coding and overview of clinical NLP challenges, even the most advanced LLMs need expert guidance to perform reliably in healthcare. That guidance comes from domain-specific structure and context, not just more data.
Grounding AI governance in clinical truth
The risks of “black box” AI are well understood. As models become more embedded in care delivery, quality measurement, and reimbursement, organizations need transparency and governance around how those models work and what they’re basing their outputs on.
That’s where a clinically informed knowledge layer isn’t just helpful; it’s essential.
Your AI strategy is only as strong as the clinical knowledge it’s built on
Healthcare’s future depends on data-driven innovation. But if you want your AI to deliver results — insights clinicians will actually use, models you can explain and stand behind, and products your customers can monetize — you need to bridge the gap between raw documentation and clinical understanding.
That’s what IMO Health does best.
We don’t replace your analytics. We make them smarter, safer, and more clinically aligned. Because in healthcare, AI is only as good as the context it’s built on.
