Generic LLMs achieve only 79% PHI detection accuracy vs 96% for domain-trained Healthcare NLP. As regulation tightens, the performance and compliance gaps are no longer acceptable.
For the past few years, the dominant assumption in healthcare AI has been one of universality: that a sufficiently powerful general-purpose language model, properly prompted, could handle clinical tasks well...
When MiBA built their oncology data curation pipeline, the technical challenge extended far beyond processing individual data types. Their system needed to ingest 1.4 million physician notes and approximately 1...
A Step-by-Step Playbook for Digitizing Every Patient Touchpoint How forward-thinking health systems are using healthcare-native AI to connect every clinical and operational workflow, from admission to discharge and beyond. Introduction:...
Healthcare systems worldwide face a growing challenge: rising patient demand, aging populations, and limited human resources. Physician shortages, burnout, and administrative overload threaten care quality, access, and sustainability. Large Language...
FHIR defines standardized resources, including Patient, Observation, Condition, Medication, Encounter, and DiagnosticReport that enable health data exchange across electronic health records, registries, analytics platforms, and artificial intelligence services. By representing...
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