Carez AI generates and stress tests synthetic datasets to ensure computer vision models perform reliably in real-world scenarios. The platform creates diverse edge-case data covering variations in lighting, motion, and scanner types to surface model blind spots. This process allows AI teams in fields like radiology and autonomous systems to prove production or clinical-grade readiness with exportable evidence.
Funding
$150K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Minimally invasive procedures rely on precise medical imaging, but current angiography suites often suffer from suboptimal contrast injections, motion artifacts, and lengthy procedure times, impacting image quality and workflow efficiency. The creation of annotated radiology datasets is a time consuming and expensive process.
Solution
Carez AI offers a software platform that leverages speech commands, computer vision, and generative AI to enhance medical image capture during minimally invasive procedures. The system optimizes contrast injections, reduces motion artifacts, and shortens procedure times by integrating directly into existing angiography suites. The Carez Data Engine uses generative AI to augment annotated radiology datasets, simulating data with unique contrast and radiation dosages. This technology provides radiologists with clearer imaging and improved workflow, while also accelerating the development of AI-powered radiology tools.
Target Audience
The primary target audience includes radiologists and interventional radiologists performing minimally invasive procedures, as well as clinical sites and research institutions involved in medical imaging and AI development.
Features
- Vendor-agnostic integration with any angiography suite
- Computer vision algorithms for real-time optimization of contrast injections
- Motion artifact reduction technology for clearer imaging
- Speech command interface for hands-free control during procedures
- Generative AI data engine for augmenting annotated angiography datasets
- Simulation of data with unique contrast and radiation dosages