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 must ensure Protected Health Information (PHI) is removed at scale and with precision. Two solutions often considered for this task are John Snow Labs’ Medical Text De-identification and Microsoft Presidio. While both are powerful tools for identifying and redacting sensitive data, they serve very different use cases — and their effectiveness in healthcare settings diverges sharply.
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...
Electronic Health Records (EHRs) hold immense potential for improving oncology care. They contain detailed histories, diagnostic findings, treatment plans, and physician notes, all of which are essential for delivering personalized...
Overview Patient adherence remains one of the toughest challenges in chronic disease management. Generic advice, like “eat healthier” or “exercise more”, often misses the mark, especially when patients face social...
Julia sat quietly, holding a printed brochure about diabetes care while her doctor talked through the usual tips she had heard many times before. At sixty-five, newly widowed and living...
Why the NLP Summit is now the Applied AI Summit Five years ago, we launched the NLP Summit with a simple mission: create a home for practitioners working on natural...
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