Study results demonstrate the accuracy of AI-based ultrasound software, enabling a quicker detection of right ventricle dysfunction caused by COVID-19, pulmonary embolism, and more
Collaboration leverages Stanford’s clinical expertise and Kheiron’s machine learning capabilities to assist radiologists in more effectively staging and monitoring treatment response of Non-Hodgkin's Lymphoma
Patient-focused Embrace® Neonatal MRI System enables safer, quicker, and easier access to critical diagnostic imaging inside the hospital’s new Neonatal Intensive Care Center.
Consistent Breast Positioning Techniques are Critical for Cancer Detection: Customized Training to Address Positioning Performance Based on Volpara Analytics Data
Studies include external validation from multiple clinical sites such as Sweden Karolinska Institute, Korea’s Seoul National University Hospital, and Netherlands Radboud University Hospital
arcc® v10.5, Apollo’s newest version of their Enterprise Imaging platform, provides encounters-based workflow for point of care ultrasound (POCUS) to aid reimbursement claims documentation
Company to Focus on Ability to Provide Creative Custom Solutions Amidst Evolving Healthcare Equipment Delivery Model, COVID and Resulting Supply Chain Challenges
arcc v10.5, Apollo’s newest upgrade of their Enterprise Imaging platform enhances clinical
workflows, interoperability, and collaboration throughout the health system
Consistent Breast Positioning Techniques are Critical for Cancer Detection: Customized Training to Address Positioning Performance Based on Volpara Analytics Data
Collaboration is a catalyst for maximizing the positive impact radiologists can make in patient care by using the latest advances in AI to automate impressions
Approval provides access to the first and only 64-slice volumetric CT digital SPECT/CT in the market,
technology for routine total body 3D imaging in nuclear medicine departments.
By eliminating the need to move neonates from the NICU to the radiology department, Aspect Imaging’s Embrace enables a safer way to perform the MRI procedure
- Study conducted by medical AI startup Lunit and Massachusetts General Hospital, published in JAMA Network Open - When used as a second reader, the AI algorithm may help detect lung cancer
- Lunit’s algorithm showed the best performance compared to other commercialized AI algorithms, ultimately reducing the workload of radiologists to classify mammography screenings
Showcased at RSNA 2020, the new fluoroscopy systems are designed to deliver rapid access to high-resolution real-time imaging of moving body structures, while reducing patient dose