FDA Radiology AI Devices

BU-CAD is a software application designed to assist physicians by analyzing breast ultrasound images to identify and evaluate soft tissue lesions suspicious for breast cancer. It highlights regions of interest and provides malignancy scores and BI-RADS classifications to support clinical decision-making. It also supports viewing of mammography images and includes tools for image adjustment and documentation, helping improve diagnostic accuracy and efficiency.

MammoScreen 2.0 is an AI-based software tool designed to assist physicians by automatically analyzing standard screening mammograms including FFDM and DBT images. It marks suspicious soft tissue lesions or calcifications on breast images and provides a level of suspicion score to help radiologists improve detection and characterization of breast cancer findings. This tool acts as a concurrent reading aid to improve diagnostic accuracy but does not replace physician judgment.

Lunit INSIGHT MMG is an AI-powered computer-assisted detection and diagnosis software designed to help radiologists detect, localize, and characterize suspicious areas for breast cancer on mammograms. It works as an adjunct tool viewed after the initial physician read, providing visual marks and scores indicating the likelihood of malignancy to assist clinical decision-making and improve breast cancer detection during mammography screenings.

breastscape v1.0 is an advanced software tool used with Olea Sphere 3.0 to assist breast imaging physicians and MRI technologists in analyzing breast MRI data. It provides visualization, semi-automatic lesion segmentation, kinetic curve analysis, and planning assistance for MR-guided breast biopsy procedures, helping clinicians detect and characterize breast lesions and plan interventions more effectively.

Transpara 1.7.0 is an AI software tool designed to assist physicians in interpreting breast imaging exams such as digital mammography and tomosynthesis. It uses deep learning algorithms to detect suspicious calcifications and soft tissue lesions, scoring their likelihood of malignancy to aid diagnosis and improve workflow. It processes images to highlight abnormal areas and provides exam-level cancer likelihood scores, supporting better identification of breast cancer indicators.

Saige-Q is a software tool that analyzes digital breast mammograms using artificial intelligence to identify exams that may contain suspicious findings suggestive of breast cancer. It helps radiologists prioritize these exams in their worklist, enabling faster review of potentially concerning cases. The tool provides passive notification codes but does not provide diagnostic decisions and supports both full-field digital mammography and digital breast tomosynthesis images.

ProFound AI Software V3.0 is an AI-based medical imaging software that aids radiologists by detecting and highlighting suspicious soft tissue densities and calcifications in 3D digital breast tomosynthesis images. It provides confidence scores that help clinicians identify potentially malignant findings more quickly and accurately during breast cancer screening and diagnosis.

The Imagio Breast Imaging System is a diagnostic tool that combines optoacoustic imaging and ultrasound to provide detailed structural and functional information about breast abnormalities. It uses AI-based software to help healthcare providers better classify breast masses, thereby improving diagnosis. The device is designed to assist, not replace, mammographic screening or biopsy.

Genius AI Detection is an AI software designed to assist radiologists by automatically identifying potential abnormalities such as masses and calcifications in digital breast tomosynthesis exams. It highlights suspicious regions on breast images, provides confidence scores, and helps improve diagnostic accuracy and workflow efficiency for breast cancer screening and diagnosis.

HealthMammo is an AI-enabled software tool designed to analyze 2D full-field digital mammograms to identify and flag suspicious findings. It assists radiologists by prioritizing mammogram cases in the PACS/worklist, helping clinicians review critical cases faster and improve workflow efficiency without replacing diagnostic evaluation.