Medical Imaging Companies
Explore 5 Medical Imaging companies in our AI directory. Leading companies include Exo Imaging, Viz.ai, Aidence.
Exo Imaging
Santa Clara, United States
Exo Imaging develops a compact, handheld ultrasound system – the Exo H1 – coupled with AI-powered workflow tools designed to democratize point-of-care ultrasound (POCUS). Their technology utilizes real-time AI to automate key tasks such as anatomy labeling, standardized view capture, and precise measurements, even functioning offline. This system aims to simplify ultrasound adoption and improve diagnostic confidence for a broad range of healthcare providers, and has been designed to replace traditional, large-scale ultrasound carts and probes.
Viz.ai
San Francisco, United States
Viz.ai is an AI-powered care coordination platform specializing in the automated detection and analysis of acute medical conditions. Their core technology utilizes over 50 FDA-cleared algorithms to analyze medical imaging – including CT scans and EKGs – and rapidly identify suspected stroke and other time-sensitive diseases. Viz.ai targets hospitals and health systems, aiming to accelerate diagnosis, optimize clinical workflows, and improve patient outcomes through faster treatment decisions.
Aidence
Amsterdam, Netherlands
Aidence develops AI software for lung cancer screening, using deep learning to analyze CT scans and detect pulmonary nodules.
Lunit
Seoul, South Korea
Lunit develops AI for cancer screening and treatment, with FDA-cleared solutions for radiology used in 5000+ hospitals worldwide.
Monai
Santa Clara, United States
Monai is an open-source, PyTorch-based framework designed to accelerate the development and deployment of AI-powered medical imaging solutions. It provides a comprehensive suite of tools – including pre-processing, model training, and evaluation – specifically tailored for analyzing modalities like MRI, CT, and X-ray images. Developed jointly by NVIDIA and King's College London, Monai is gaining traction within the research community and is being utilized in projects focused on disease detection, segmentation, and image registration, with a growing ecosystem of contributed workflows and models.