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Medical AI Applications Blog

Generate grounded DICOM de-identification pipelines with the Visual NLP DICOM Skill, supporting metadata, pixel, free-text, PDF, and zero-shot PHI de-identification workflows.

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DICOM de-identification is workflow-specific because PHI can appear in metadata tags, free-text metadata fields, burned-in image pixels, and encapsulated PDF content. A production pipeline may need to inspect tags, apply...

Why radiology AI adoption stalls - and what health systems that scaled it did differently By 2055, US imaging demand will rise 16.9%–26.9% above 2023 levels, while the radiologist workforce...

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...

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...
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