CogNet QmTRIAGE

by MedCognetics  · Based in United States → — Unbiased AI Transforming Healthcare
Oncology Radiology
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Not disclosed
Regulatory Status Disclosed

Overview

MedCognetics’ CogNet QmTRIAGE is an advanced artificial intelligence (AI) software platform designed to integrate into radiology workflows for breast cancer screening and triage. It utilizes machine learning algorithms to analyze 2D FFDM (Full-Field Digital Mammography) and DBT (Digital Breast Tomosynthesis) exams, flagging suspicious findings to help radiologists prioritize their worklist.

The platform aims to improve early breast cancer detection, particularly across diverse patient populations, by employing unbiased algorithms trained on global datasets. CogNet QmTRIAGE functions as a passive notification and prioritization-only tool, enhancing clinical efficiency and reducing radiologist burnout by streamlining the review process.

Beyond cancer screening, the technology can also analyze tissue for the presence of breast arterial calcifications, an indicator related to cardiovascular health. It supports flexible deployment options, including on-premise, cloud-based, or via the web.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • AI-driven triage for mammography images (FFDM and DBT)
  • Flags suspicious cases for prioritized review
  • Integrates into existing radiology workflows (PACS/workstations)
  • Utilizes advanced AI and machine learning algorithms
  • Designed to detect early signs of cancer across all ethnicities with unbiased algorithms
  • Reduces radiologist review time and burnout
  • Can be deployed on-premise, in the cloud, or via the web
  • Analyzes breast arterial calcifications (beyond cancer screening)

Use Cases

  • Prioritizing mammography exams with suspicious findings
  • Improving early breast cancer detection
  • Reducing radiologist workload and burnout
  • Enhancing clinical efficiency in radiology departments
  • Addressing disparities in cancer detection across diverse patient groups
  • Facilitating faster patient care by enabling same-day additional imaging or biopsies

What Physicians Need to Know

Key Capabilities
CogNet QmTRIAGE is an AI-powered software designed to analyze mammography images (both 2D full-field digital mammography and 3D digital breast tomosynthesis) and flag suspicious cases for prioritized review by radiologists. It aims to improve the efficiency and accuracy of breast cancer detection. The system also offers features like image denoising and motion suppression to enhance image clarity. Additionally, it includes a patient risk scoring feature that uses imaging biomarkers to estimate individualized breast cancer risk, and a breast arterial calcifications analysis feature, which is currently under development.
Clinical Utility
The primary clinical utility of CogNet QmTRIAGE is to empower radiologists to triage mammography cases more effectively, ensuring that suspicious findings are reviewed first. This helps manage heavy workloads and radiologist burnout, especially in regions with radiologist shortages. By flagging potentially cancerous cases, it aims to facilitate earlier detection of breast cancer, which can decrease mortality. The software is designed to perform reliably across diverse patient populations, addressing health equity concerns.
Integration Options
CogNet QmTRIAGE is designed to integrate into existing imaging and IT systems, including PACS or workstation workflows. It can be deployed on customer premises, via the cloud, or through the web. The system can also be embedded directly into mammography units, eliminating latency and reducing reliance on external servers or the cloud. MedCognetics has partnered with AMD to optimize its AI solution for AMD hardware and software, utilizing AMD Ryzen™ processors and ROCm™ software. It also uses the NVIDIA IGX Orin medical-grade edge AI platform for embedded AI image processing.
Compliance Status
CogNet QmTRIAGE (initially QmTRIAGE) received FDA 510(k) clearance in September 2022 for radiological computer-aided triage and notification. The enhanced version, CogNet AI-MT+, also received FDA 510(k) clearance in February 2026, expanding its regulatory footprint in AI-driven breast imaging. It is cleared under 21 CFR 892.2080 (Radiological Computer Aided Triage and Notification Software), Class II, Product Code QFM.
User Experience
The software aims to optimize workflows for radiologists by prioritizing suspicious mammograms, which can reduce review time. The embedded AI system offers immediate results and improved image quality. The goal is to provide a user-friendly system that enhances efficiency and accuracy.
Implementation Complexity
The system is designed for integration into existing imaging and IT systems, and can be deployed flexibly (on-premises, cloud, or web). The embedded AI solution aims to simplify implementation by being built directly into mammography systems, offering easier upgrades and maintenance. MedCognetics also offers tools for medical imaging AI development, including data curation, annotation, synthetic data, and model evaluation, which could suggest a comprehensive approach to implementation and customization.
Evidence Base
CogNet QmTRIAGE's algorithms are trained on diverse global datasets, including de-identified clinical data from UT Southwestern Medical Center and intellectual property from UT Dallas' Quality of Life Technology Laboratory. This diverse training aims to mitigate data bias and ensure reliable performance across various ethnicities and geographies. MedCognetics has received NIH support for unbiased AI studies. The company has also been awarded a patent for its medical imaging AI technology in mammography.
Physician Tip

Leverage CogNet QmTRIAGE to prioritize your mammography workload, focusing on cases flagged as suspicious by the AI. This can help manage high volumes and reduce burnout. While the AI assists in identifying potential issues, always combine its findings with your clinical judgment and critical thinking skills. Utilize the system's ability to detect subtle cancer patterns and enhance image clarity to improve diagnostic accuracy. Be aware of the system's FDA clearance for triage and notification, and understand its role as a tool to support, not replace, your expertise. The unbiased nature of the AI, trained on diverse datasets, aims to provide equitable results across patient populations.

CogNet QmTRIAGE is designed for flexible integration into existing radiology workflows, including PACS and workstations. It supports various deployment options: on-premises, cloud, or web. For enhanced performance and reduced latency, the system can be embedded directly into mammography units, utilizing hardware from partners like AMD and NVIDIA.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Not disclosed
DeploymentFlexible Deployment
Compliance
BAA AvailableUnknown
HIPAA Compliant Yes AI-estimated
FDA Status Cleared AI-estimated
Integrations
EHR Not specified
Specialties Oncology, Radiology

What the Web Says

CogNet QmTRIAGE is an AI-powered software developed by MedCognetics for breast cancer screening. It analyzes 2D full-field digital mammography screenings to identify and flag potential abnormalities, aiming to assist radiologists in prioritizing their workload and improving early detection, especially across diverse patient populations. The software integrates into existing radiology workflows, layering its findings onto mammography images without requiring additional tasks from radiologists.

Overall: Positive

Strengths

  • Aids in early breast cancer detection.
  • Helps radiologists prioritize cases, potentially reducing review time and burnout.
  • Trained on diverse datasets to reduce bias and improve outcomes across various ethnicities.
  • Integrates seamlessly into standard radiology workflows.
  • FDA 510(k) cleared.
  • Aims to detect small or obscured tumors within dense breast tissue.

Limitations

  • No specific cons were found in the provided search results from physicians, healthcare IT, tech reviewers, Reddit, G2, or Capterra.
  • AI is not yet ready to replace trained practitioners.

Based on reviews from: mobihealthnews.com, pharmaphorum.com, dallasinnovates.com, auntminnie.com, fda.gov, gencat.cat, parikhradiology.com, parse.gl, aihealthcarenow.com, kjfp.or.kr, fda.report, euskadi.eus

Last updated: 2026-10-31

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Press & Coverage

radiologybusiness.com
MedCognetics CogNet AI-MT technology is the first embedded AI cancer detection system built into the mammography system to eliminate eliminates latency and delivering immediate, high-quality image analysis and can help prioritize exams in the worklist.
MedCognetics successfully tested and implemented the first embedded AI cancer detection system, CogNet AI-MT, directly into a mammography system, aiming to provide immediate image analysis and prioritize exams, thus reducing patient anxiety and speeding up workflows. This upgraded technology, which is FDA 510(k) cleared for mammogram image triage, utilizes advanced AI and machine learning to detect early signs of cancer across all ethnicities and can be deployed on-premise, in the cloud, or via the web.
2024-10
orthogonal.io
List of FDA-Cleared SaMD
CogNet QmTRIAGE by Medcognetics, Inc. received FDA clearance on September 29, 2022, for radiology applications.
2024-10
manuals.plus
VinDr-Mammo Indications for Use
VinDr-Mammo, a passive notification software for prioritizing suspicious findings in mammograms, uses CogNet QmTRIAGE (K220080) as its predicate device. This indicates that CogNet QmTRIAGE is a recognized and established technology in the field of AI-powered mammography triage.
2024-09
healthcareaiops.com
FDA 510(k) K220080: CogNet QmTRIAGE by Medcognetics, Inc., cleared 2022-09-29 under AI-associated product code QFM. Public record, sourced.
CogNet QmTRIAGE by Medcognetics, Inc. received FDA 510(k) clearance (K220080) on September 29, 2022, under the AI-associated product code QFM.
2026-08
fda.gov
CogNet QmTRIAGE FDA Listing
Medcognetics, Inc. is listed as the applicant for CogNet QmTRIAGE, with Diane Rutherford as the contact. The device is regulated under number 892.208023 with classification product code QFM, falling under the Radiology medical specialty.
2026-10
kjfp.or.kr
AI in Breast Imaging
CogNet QmTRIAGE by MedCognetics, Inc. is listed among FDA-approved AI-CADs for breast imaging, specifically for breast lesion characterization in mammography. The article discusses the increasing role of AI in breast imaging, including its use in detecting breast cancer, assisting radiologists, and improving workflow.
2023-12
euskadi.eus
Inteligencia artificial en el programa de cribado de cáncer de mama
CogNet QmTRIAGE from Med Cognetics is identified as an FDA-approved AI device for classifying and notifying radiological findings in breast cancer screening, under regulation 21 CFR 892.2080. This information is part of a report on artificial intelligence in breast cancer screening programs.
2022-10
gencat.cat
The Role of Artificial Intelligence in Understanding and Addressing Disparities in Breast Cancer Outcomes
MedCognetics (CogNet) QmTRIAGE™ software is described as a passive notification tool for prioritization-only, parallel-workflow, utilized by MQSA qualified interpreting physicians to prioritize patients with suspicious findings in medical care. The software uses an AI algorithm to analyze 2D FFDM screening mammograms and flags studies suggestive of suspicious findings at the exam level, producing an output to a PACS/Workstation for flagging.
2021-04

Videos

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Frequently Asked Questions

CogNet QmTRIAGE is designed to integrate seamlessly into standard radiology workflows. It analyzes screening mammograms (both 2D and 3D) and flags exams that suggest the presence of suspicious findings. This allows for worklist prioritization within your PACS or workstation, optimizing your workflow without requiring additional tasks.
CogNet QmTRIAGE has received U.S. FDA 510(k) clearance as an AI-enabled software for breast cancer screening. It is cleared for radiological computer-aided triage and notification, specifically for mammogram images.
MedCognetics emphasizes that CogNet QmTRIAGE was developed with a focus on reducing bias. The AI has been trained on diverse global datasets, including data from various ethnicities and races across the United States, UK, Europe, Middle East, Asia, South America, and Africa, to ensure its effectiveness across a broad patient population.
Several AI-powered solutions exist for breast cancer screening. Some alternatives mentioned in the context of FDA clearance include Qlarity Imaging's QuantX and Zebra Medical's HealthMammo. CogNet QmTRIAGE differentiates itself by its focus on unbiased AI trained on diverse datasets and its ability to be deployed on-premises, via the cloud, or through the web.
The provided information does not contain specific details about the pricing model or licensing structure for CogNet QmTRIAGE. For such information, it would be best to contact MedCognetics directly.

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Suggest an Edit → | Report an issue | Last Verified: 2026-04-18 | First Added: 2026-04-18
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