What hospitals need to become AI-ready: data infrastructure, governance, workflows, and staffing, with evidence from Providence and Intermountain Health.
The AI-Ready Hospital: Architecture, Culture, Workflows, and Staffing for the Next Decade An AI-ready hospital is a health system whose data infrastructure, governance, clinical workflows, and staffing are built to...
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...
Hand a language model a difficult clinical case and it will hand you a diagnosis. Quickly, fluently, with an air of total certainty. Quite often it will also be wrong,...
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...
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