As the healthcare industry wrestles with how to best use AI (augmented intelligence) to transform raw data into actionable insights, one of the keys to success is ensuring that data is semantically interoperable – that clinical terms can be unambiguously transmitted while preserving the underlying clinical intent.

However, achieving semantic interoperability is complex and challenging, especially when it comes to healthcare data. And while using AI technologies like large language models (LLMs) holds great promise, if they lack proper training on robust clinical terminology, their potential – and efficacy – may be limited.


Key takeaways

• Understanding LLMs: This brief provides an overview of LLMs and their significance (and limitations) in processing and deciphering complex clinical narratives.

• The crucial role of semantic interoperability: Discover why maintaining the intended meaning of data is the cornerstone of accurate AI insights in healthcare and how semantic interoperability preserves clinical intent for informed decision-making.

• Optimizing LLMs for real insights: For LLMs to offer accurate interpretations, integrate data seamlessly, and provide actionable insights that enhance patient care and decision-making – an understanding of comprehensive clinical terminology is necessary.