The Challenges of Regulatory-Grade De-Identification at Scale Healthcare organizations face a critical dilemma: vast volumes of patient data: free-text notes, structured fields, clinical images, even audio/video are invaluable for research,...
Converting free-text medical descriptions into structured ontology codes with validation Human phenotypes, observable traits and clinical abnormalities like “short stature” or “muscle weakness” are crucial in diagnosing diseases, especially in...
Why is it hard to use clinical guidelines during patient care? Clinical guidelines are foundational to evidence-based care, yet their length and complexity often make them impractical to consult during...
Discover how John Snow Labs enables secure, scalable DICOM de-identification using AWS HealthImaging and SageMaker. [embed]https://www.youtube.com/watch?v=ubfwki4J8UA[/embed] What is the most secure way to de-identify DICOM files in AWS? To share...
What is the purpose of integrating medical LLMs for patient journeys? Integrating Document Understanding, Reasoning, and Conversational Medical LLMs (Large Language Models) enables healthcare organizations to construct longitudinal, context-rich patient...
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