FDA Radiology AI Devices

BoneView by Gleamer is an AI-powered software that analyzes X-ray images of bones to detect and highlight fractures. It acts as a concurrent reading aid for clinicians, improving the accuracy and confidence of fracture diagnosis from radiographs across a wide range of anatomical areas. By providing clear visual annotations, it helps reduce missed fractures and supports better patient care.

Annalise Enterprise CXR Triage Pneumothorax is a software tool that uses artificial intelligence to analyze adult chest x-rays for signs of pneumothorax and tension pneumothorax. It assists trained clinicians by automatically prioritizing and triaging cases in their worklist, enabling faster attention to urgent findings. The device integrates with hospital imaging systems and helps optimize workflow without replacing clinical judgment.

The Micro C Medical Imaging System, M01 by OXOS Medical, Inc. is a handheld, portable mobile X-ray device intended for use by trained clinicians to capture diagnostic images of extremities. It features an AI-based software called AiLARA that recommends power settings and exposure times to optimize image quality and reduce retakes, enhancing clinician efficiency and patient safety.

Eclipse II with Smart Noise Cancellation is a software module integrated within the Carestream ImageView application that enhances digital radiography images by reducing noise using a Convolutional Neural Network. It supports various Carestream digital radiography detectors. This technology allows for up to 50% dose reduction in certain detector types without sacrificing image quality, helping clinicians obtain clearer x-ray images at lower radiation doses, thus improving patient safety and diagnostic confidence.

qXR-BT by Qure.ai Technologies is an AI-powered software designed to analyze chest X-ray images to help physicians confirm the position of breathing tubes. It generates secondary digital images with highlighted tube tips and anatomical landmarks, providing valuable adjunctive information during radiological assessment.

HealthPPT is an AI-powered software tool that helps radiologists by automatically detecting signs of pneumoperitoneum in adult frontal chest X-rays. It prioritizes urgent cases by notifying the radiology workstation with a low-resolution preview image, enabling faster review of potentially critical findings. The device works alongside standard workflows without altering original images and does not itself diagnose the condition, but aids clinical assessment by highlighting suspicious cases for prompt review.

Lunit INSIGHT CXR Triage is an AI-based software tool designed to analyze adult chest X-ray images and identify potentially critical lung conditions such as pleural effusion and pneumothorax. It flags suspicious cases in the radiology workflow, enabling radiologists to prioritize urgent cases more efficiently without altering the standard reading queue. It operates as a supportive notification system and is not intended for standalone clinical decision making.

Critical Care Suite with Endotracheal Tube Positioning AI Algorithm is a software tool that uses AI to automatically analyze frontal chest X-rays. It detects and localizes the position of an endotracheal tube and calculates the distance between the tube tip and the carina, providing clinicians with important information on tube placement to improve patient care.

Intelligent NR is a software function integrated with Canon's CXDI Control Software that uses machine learning-based noise reduction to enhance the quality of digital X-ray images. It helps clinicians obtain clearer images from conventional radiographic exams, improving diagnostic confidence especially in trauma, intensive care, and pediatric settings.

Auto Lung Nodule Detection is a computer-aided detection software designed by Samsung Electronics to assist physicians in identifying and marking suspected pulmonary nodules between 10 and 30 mm on PA chest X-rays. Integrated into Samsung's digital X-ray systems, it serves as a second reader by highlighting regions of interest on images to support radiologists in their interpretation and improve detection accuracy.