Bridging human and artificial intelligence to advance medical diagnostics
Bridge human and artificial intelligence to advance medical diagnostics to improve patient outcomes around the world.
A world in which radiologists are empowered with the most advanced medical diagnostic tools to facilitate optimal patient care and support.
Advanced technology that integrates seamlessly into any existing health system infrastructure to improve workflow, efficiency, and quality at scale.
The Enlitic Curie™ platform integrates seamlessly into many stages of radiological workflow.
RESEARCH USE ONLY
TECHNOLOGIST IMAGES PATIENT
Proprietary AI algorithms automatically add
uniform description labels to every DICOM
before transmission to PACS, increasing
efficiency through consistent, complete, and
RADIOLOGIST READS IMAGE
RADIOLOGIST SUBMITS REPORT
Triage & Detect
Our models read studies alongside radiologists, detecting rare and subtle findings, providing measurements and descriptions, automating longitudinal analysis, and even generating reports.
Review & Learn
Our models can provide a post-read analysis, checking a radiology report against the corresponding images to help prevent over or under-called findings.
Who we are
Enlitic develops clinical and non-clinical AI-powered workflow solutions for radiologists who want the most comprehensive technology, without the complexity. The Enlitic Curie™ platform automatically standardizes data, and provides a host of AI-powered solutions for reading, reporting, QA and research. With Curie, data is simplified, and integration is seamless.
In a clinical study, our chest CT models were found to often help radiologists identify malignant lung nodules up to 18 months sooner.
Optimized for speed, our models can interpret X-ray, CT, and MRI studies in 15 milliseconds per image.
We individually qualify each radiologist who trains and validates our models, with a team of more than 65 active around the world.
In a clinical study, radiologists read cases 21% faster when augmented by Enlitic's wrist fracture detection model.