From Data Deluge to Drug Insight: Purpose-Built AI as the Catalyst for Next-Generation Therapeutics

Unstructured clinical data (which doesn’t conform to structured fields like drop-downs or checkboxes) includes clinical notes, imaging reports, physician narratives, device data, and patient feedback and account for roughly 80% of US health records. These data provide a wealth of insights that can accelerate the development of effective drug therapies. Yet, we’re relying on people and generic AI to wade through the billions of clinical records created each year, 97% of which go unused. 

As drug developers rush to adopt AI, cracks in this approach are surfacing—from hallucinated findings (fabricated or inaccurate AI-generated content) to acronym mix-ups that threaten pharmacovigilance, real-world-data (RWD) studies, and discovery research. To use RWD to its full potential, pharma needs purpose-built, clinically trained AI that understands medical terminology, recognizes and preserves document context, and excels under human supervision.

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