By combining top-tier model accuracy, hybrid masking/obfuscation for context preservation, cost-effective deployment, and certification-backed results, John Snow Labs enables:Fully automatic de-identification across text, images, structured data, without human reviewers.PHI-free datasets validated under HIPAA expert-determination, ready for monetization, RWD, and research.Enterprise-scale pipelines capable of processing billions of notes cost-effectively.For healthcare orgs, data aggregators, and researchers, this isn’t just technology, it’s a revolution in how protected data can be shared, analyzed, and commercialized securely, legally, and at scale.
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,...
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...
As healthcare organizations increasingly rely on unstructured data like clinical notes, pathology reports, and discharge summaries, de-identifying patient information becomes mission-critical. Whether for research, AI training, or compliance, healthcare providers...
In today’s hyper-connected world, every organization is a data company, whether they realize it or not. From hospitals and banks to startups and SaaS platforms, sensitive data flows through every...
Introduction In the evolving landscape of artificial intelligence for healthcare, John Snow Labs continues to demonstrate exceptional leadership with the release of the very first Medical Vision-Language Model (VLM). This model,...
Demos
See More