FDA Radiology AI Devices

MammoScreen® is an AI-based software designed to assist radiologists in detecting and diagnosing lesions that may be indicative of breast cancer from mammography images. It helps improve accuracy and efficiency by highlighting suspicious areas, supporting earlier and more reliable cancer detection.

See-Mode Augmented Reporting Tool, Thyroid (SMART-T) is an AI-powered software that assists medical professionals in analyzing thyroid ultrasound images by automatically localizing thyroid nodules and characterizing them using standardized descriptors based on ACR TI-RADS. It helps clinicians generate structured reports and improves accuracy in detection and classification of nodules, thereby supporting clinical decision-making in thyroid nodule management.

MammoScreen 3 is an artificial intelligence software tool designed to assist physicians in interpreting screening mammograms from digital mammography and digital breast tomosynthesis systems. It provides graphical marks indicating suspicious soft-tissue lesions or calcifications, along with a suspicion score and lesion characterization, helping radiologists in their diagnosis and reporting. The software is intended as an aid to, but not a replacement for, the physician's clinical judgment.

Lunit INSIGHT DBT is an AI-powered software designed to assist physicians in detecting and characterizing suspicious lesions for breast cancer in digital breast tomosynthesis (DBT) images. It analyzes DBT scans to identify and highlight regions of soft tissue lesions and calcifications with scores indicating malignancy likelihood, thereby supporting better screening and diagnosis of breast cancer in women.

Genius AI Detection 2.0 with CC-MLO Correlation is an AI-powered software designed to assist radiologists by analyzing digital breast tomosynthesis images to detect and mark suspicious soft tissue densities and calcifications. It provides confidence scores for each detected lesion, helping clinicians interpret breast cancer screening exams more effectively, including correlation between different standard mammographic views for better diagnosis workflow.

Genius AI Detection 2.0 is a software device that uses deep learning AI models to analyze digital breast tomosynthesis (DBT) images. It identifies and marks suspicious soft tissue densities and calcifications, providing confidence scores to aid radiologists during breast cancer screening. The AI improves detection specificity and reduces false positives, supporting clinicians in interpreting 3D mammography exams efficiently and accurately.

Transpara 1.7.2 is an AI-based software tool that helps radiologists detect and assess suspicious regions in breast mammography and tomosynthesis exams. It highlights potential cancerous lesions, scores their likelihood of malignancy, and provides an overall exam score to assist in clinical decision making, enhancing the accuracy and efficiency of breast cancer screening.

ProstatID is an AI software tool designed to assist radiologists by analyzing prostate MRI scans to identify and assess suspicious lesions that may indicate prostate cancer. It processes MRI images and provides detection overlays, lesion likelihood scores, 3D renderings, and a summary report to support diagnostic decisions for high-risk screening or diagnostic imaging.

Saige-Dx is an AI-based software that analyzes digital breast tomosynthesis mammograms to detect suspicious soft tissue lesions and calcifications that may indicate cancer. It provides radiologists with a suspicion level for each finding and the entire case, helping improve detection accuracy and reduce reading time during breast cancer screening.

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.