Large language models (LLMs) have captured the spotlight with their ability to generate fluent, contextual responses across a wide range of medical use cases. But beneath the hype, a quieter...
When Ohio State University built their Medical LLM infrastructure to process over 200 million clinical notes, the technical challenges extended far beyond preventing hallucinations. Their system required unified data ingestion...
Why annotated datasets lose value when schemas change»: «A health system invests years building diagnosis extraction data, de-identification masksReusing clinical annotations across projects means importing existing labeled datasets into a...
A radiology AI team has 5,000 chest X-rays ready to annotate. The images sit in the hospital's imaging archive, stored the way every radiology department stores them: as DICOM files. ...
Most annotation platforms restrict teams to a single LLM provider. For healthcare and life sciences organizations, this creates compliance risk, cost inefficiency, and limits the use of domain-specific models required...
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