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qXR-Detect

by Qure.ai Technologies · Based in India → — Using artificial intelligence to make healthcare more accessible and affordable.

Emergency Medicine Pulmonology Radiology

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Overview

qXR-Detect by Qure.ai Technologies is an artificial intelligence tool designed for the automated interpretation of chest X-rays.

It is primarily intended for radiologists, emergency physicians, and other clinicians in various care settings, including hospitals, clinics, and teleradiology practices, who routinely interpret chest X-rays.

The tool integrates into existing clinical workflows by providing an automated analysis of chest X-ray images. It can be used as a triage tool to flag abnormal scans for urgent review or to assist in the detection of various pathologies. This can potentially streamline the interpretation process, especially in high-volume environments.

Notable capabilities of qXR-Detect include the identification of multiple findings on chest X-rays, such as tuberculosis, pneumonia, and other lung abnormalities. It is designed to highlight areas of concern on the image, providing a visual aid to the interpreting physician.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

Automated abnormality detection on chest radiographs
Identification and localization of up to 29 common abnormalities
Pre-read assistance with AI overlays and pre-filled reports
Visual localization and explainability (bounding boxes, region-of-interest labels)
Triage and notification of critical findings (e.g., pneumothorax, pleural effusion)
Active case finding for TB and lung cancer screening
Detection of incidental findings for lung cancer and heart failure
Classification of normal X-rays to clear backlogs
Multilingual support on mobile and web
Integration into existing standard of care workflow as a passive notification system

Use Cases

Early identification of lung nodules and other abnormalities on chest X-rays
Support for emergency room physicians, family medicine practitioners, and radiologists
Triage of suspect patients in resource-constrained countries
Screening for Tuberculosis and lung cancer
Monitoring progression of infection and evaluating treatment response
Reducing turnaround time for reporting in radiology workflows

What Physicians Need to Know

Evidence Base

qXR-Detect is a deep learning AI solution trained on a massive dataset of 3.5 million anonymous X-rays from 45 global locations, including in-hospital and outpatient settings. [7, 8] It is designed to detect and highlight abnormalities in chest radiographs. [2] The algorithm has been adapted to identify findings related to COVID-19 infections, trained on a dataset of 2.5 million chest X-rays that included bacterial and viral pneumonia. [28]

Clinical Validation Studies

qXR-Detect has undergone rigorous clinical validation, including standalone and multi-reader multi-case (MRMC) clinical studies to achieve FDA clearance. [1, 10, 23] In a study evaluating its performance in detecting malignant nodules, qXR demonstrated high accuracy with an AUC of 0.99, 90% specificity, and 100% sensitivity at the operating threshold. [7] For detecting nodules, sensitivity was 99% and specificity ranged from 87% to 92%. [7] Another prospective multicenter quality improvement study involving 65,604 chest X-rays showed a high negative predictive value of 98.9% for detecting both abnormal and normal chest X-rays. [8] The qXR algorithm has shown up to 96% sensitivity and 100% specificity for pneumothoraces and pleural effusions in a multicenter publication. [11] qXR-LN (Lung Nodule) demonstrated statistically significant and clinically meaningful enhancement in pulmonary nodule detection across various reader groups, including radiologists, pulmonologists, and emergency room physicians. [16]

Alert Fatigue Management

The system provides visual localization and explainability through bounding boxes and region-of-interest labels, helping radiologists quickly understand the reason for an alert. [1, 5, 10] This approach aims to go beyond binary detection and offer practical frontline support. [1, 5, 10]

Differential Diagnosis Support

qXR-Detect identifies, highlights, and categorizes key positive findings on plain film chest X-rays across six regions of interest: lung, pleura, mediastinum/hila & heart, bone, hardware, and others. [1, 5, 10, 17, 23] It can detect and localize up to 29 common abnormalities, including lung nodules and signs of tuberculosis. [6, 8, 13, 15] The software can also help differentiate benign and malignant nodules by analyzing characteristics such as size, spiculation, calcification, homogeneity, and solitary nodules. [2] Additionally, qXR-HF facilitates early detection of heart failure by identifying indicative patterns like cardiomegaly, abnormal cardiothoracic ratio, and pleural effusion. [8]

Guideline Update Frequency

qXR-Detect is the only chest X-ray CADe device cleared by the FDA with a Predetermined Change Control Plan (PCCP). [1, 10, 17, 23] This plan ensures that US health system customers have access to the most up-to-date version of the algorithm as models and architecture evolve, without requiring additional clearance. [1, 10, 17, 23]

Clinical Workflow Integration

qXR-Detect integrates into existing radiology workflows, utilizing image viewing and reporting tools. [2, 9] It functions as a post-processing image analysis application and a concurrent reading aid. [2, 6] The system provides pre-read assistance, pre-filled reports, and AI overlays to augment productivity. [9, 12] It can also help clear backlogs by segregating unremarkable chest X-rays and serves as a passive notification system for worklist prioritization. [9, 12, 13]

Decision Audit Trail

While specific details about qXR-Detect's internal audit trail for decision-making are not explicitly provided, clinical imaging systems generally require secure, time-stamped electronic records of all actions performed on imaging data to ensure data integrity and regulatory compliance (e.g., FDA 21 CFR Part 11, GxP, EMA Annex 11). [26] An auditable framework for clinical AI decision support typically involves recording the original user query, patient data elements accessed, unique identifiers of retrieved sources, and the exact steps the reasoning engine took to derive a suggestion. [27]

Physician Tip

qXR-Detect serves as a valuable support tool for emergency room physicians, family medicine practitioners, and radiologists by providing early identification, highlighting, and categorization of key positive findings on plain film chest X-rays. [1, 5, 10, 17] The visual localization and explainability features (bounding boxes, region-of-interest labels) help in quickly understanding the alerts. [1, 5, 10] It can significantly reduce workload, improve report accuracy, and decrease turnaround time, especially in settings with reporting backlogs or limited radiologists. [14] However, it is crucial to remember that qXR-Detect is intended as a support tool and not a diagnostic device or a source of medical advice for determining treatment plans. [2, 6] Clinicians are responsible for viewing the original chest X-rays as per the standard of care and using the AI results in conjunction with other patient information and professional judgment. [6]

qXR is compatible with all Radiology IT systems (vendor-neutral), including PACS, Viewer, and RIS systems. [2] It provides annotated X-rays and structured reports in DICOM and JSON formats. [3, 12] The system can be deployed on cloud or on-premise, with multi-region/cloud and on-prem deployment options. [9, 12] Qure.ai has integration partners including GE Healthcare, Philips Healthcare, Siemens Healthineers, Canon Medical Systems, Fujifilm Medical Systems, Carestream Health, Agfa HealthCare, Sectra, Merge Healthcare, and McKesson Corporation. [12]

At a glance

Deployment Cloud Or On Premise

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Details

Category	Clinical Decision Support & Reference, Radiology & Imaging AI
Pricing	Unknown — unknown
Deployment	Cloud or on-premise
Compliance
BAA Available	Unknown AI-estimated
HIPAA Compliant	Yes AI-estimated
FDA Status	Cleared AI-estimated qXR-Detect has received 510(k) Class II FDA clearance as a computer-assisted detection (CADe) radiography solution. This clearance covers the identification, highlighting, and categorization of key positive findings across six regions of interest on plain film chest X-rays: Lung, Pleura, Mediastinum/Hila & Heart, Bone, Hardware, and Others. qXR-Detect is also the only chest X-ray CADe device cleared by the FDA with a Predetermined Change Control Plan (PCCP), allowing for algorithm updates without requiring additional clearance.
Integrations
EHR	Not specified
Specialties	Emergency Medicine, Pulmonology, Radiology

What the Web Says

qXR-Detect by Qure.ai is a deep learning AI technology designed to assist physicians, including radiologists, emergency room physicians, and family medicine practitioners, in interpreting chest X-rays. It aims to improve efficiency, accuracy, and turnaround time by detecting and localizing abnormalities, such as lung nodules, cardiomegaly, and pleural effusions. The software is compatible with various Radiology IT systems and has received FDA 510(k) class II clearance for multiple indications.

Overall: Positive

Strengths

High accuracy in detecting and localizing abnormalities, including lung nodules, with studies showing high sensitivity and specificity.
Can detect over 30 different chest X-ray findings, including signs of tuberculosis and lung cancer.
Provides visual localization and explainability through bounding boxes and region-of-interest labels, helping radiologists understand alerts.
Integrates with existing radiology workflows and is compatible with various IT systems (PACS, Viewer, RIS).
Aids in early identification and prioritization of critical cases, potentially reducing turnaround time in emergency settings.
FDA 510(k) class II cleared for multiple indications, including a Predetermined Change Control Plan (PCCP) for algorithm updates.

Limitations

Some physicians have reported that the AI can overcall opacities, fibrosis, and pneumothorax, leading to clinically irrelevant details that slow down radiologists.
One physician noted that the nodule detection software sometimes misidentifies skin lesions and pulmonary arteries as nodules.
Concerns exist about ordering providers seeing AI outputs directly, as it could lead to them copying AI findings without proper review.
The technology is intended as a support tool and not a substitute for medical advice or a radiologist's interpretation.
The transparency of AI updates and performance monitoring to clinicians and patients remains limited despite PCCP clearance.

Based on reviews from: Health Technology Review, FDA Clears Six Indications for Qure.ai's Chest X-Ray Reporting Tool, AI Tools Every Physician Should Know, Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment, Qure AI Reviews 2026: Details, Pricing, & Features - G2, qXR: AI for Blazing Fast Reporting on Chest X-Rays | TestDynamics, Is QXR the best release group for special features? : r/trackers - Reddit, Qure.ai Reviews, Pricing, Features & Integrations - Elion, Are QxR the best for 1080p movies or is there something better? : r/Piracy - Reddit, Just saw my first AI CXR interpretation : r/doctorsUK - Reddit, Radiologists using AI tools -what's your experience with Qure.ai vs alternatives like AIdoc or Viz.ai? : r/radiologyAI - Reddit, I appreciate qxr so much, going and doing the leg work checking stuff so we get the best : r/Piracy - Reddit, QXR Software Pricing, Alternatives & More 2026 | Capterra, December 22, 2023 Qure.ai Technologies Sri Anusha Matta Senior Regulatory Affairs Manager Level 7, Commerz II, International, Capterra Reviews from Real Users - TrustRadius, Qure.ai receives 510(k) FDA clearance for x-ray solution, Regulating AI in healthcare: a moving target - The BMJ, Capterra Pros and Cons | User Likes & Dislikes - G2, Capterra: Reviews, Pricing, Features in 2026 - SoftwareSuggest, Detect Reviews 2026: Details, Pricing, & Features | G2, QZZR Reviews 2026. Verified Reviews, Pros & Cons - Capterra, Q Research Software by Displayr Reviews 2026: Details, Pricing, & Features | G2, Qwairy Reviews 2026: Details, Pricing, & Features - G2

Last updated: 2026-08-19

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Press & Coverage

Qure.ai

Qure.ai Secures Six New FDA Clearances for qXR-Detect

Qure.ai announced six new FDA 510(k) Class II clearances for qXR-Detect, its AI-powered computer-assisted detection (CADe) solution for plain film chest X-rays. This clearance allows for the early identification, highlighting, and categorization of key positive findings across six regions of interest on chest X-rays.

2026-02

AuntMinnie

Qure.ai secures FDA clearance for chest x-ray CAD software | AuntMinnie

Qure.ai has received 510(k) clearance from the U.S. FDA for qXR-Detect, a CAD solution for chest X-rays that identifies and categorizes positive findings in six regions. This clearance completes Qure.ai's full qXR product suite and includes a predetermined change control plan for algorithm updates.

2026-02

Medical Product Outsourcing

Qure.ai Earns FDA OK for 6 New AI Chest X-ray Indications - Medical Product Outsourcing

Qure.ai's qXR-Detect has received FDA 510(k) clearance for six new indications, completing its qXR suite of AI algorithms for chest X-ray analysis. The technology aids in early identification, highlighting, and categorization of key positive findings on plain film chest X-rays.

2026-02

The BMJ

Regulating AI in healthcare: a moving target - The BMJ

Qure.ai's qXR-Detect, a chest radiography triage tool, is noted as the first device cleared under a Predetermined Change Control Plan (PCCP) in the US, allowing for algorithm updates without repeated regulatory review.

2026-08

BioMedInformatics / Semantic Scholar

Evaluation of the 'qXR' Software for the Detection of Pulmonary Nodules, Cardiomegaly and Pleural Effusion: A Comparative Analysis in a Latin American General Hospital - Semantic Scholar

This study evaluated qXR's performance in detecting pulmonary nodules, cardiomegaly, and pleural effusion in a Latin American hospital, finding moderate diagnostic agreement with radiologists for nodules and pleural effusion, and lower agreement for cardiomegaly.

2026-03

Imaging Technology News

FDA Clears Six Indications for Qure.ai's Chest X-Ray Reporting Tool

Qure.ai's qXR-Detect has received FDA clearance for six indications, enabling the AI tool to identify, highlight, and categorize key positive findings on chest X-rays. The tool provides visual localization and explainability to assist radiologists in understanding alerts.

2026-02

IndiaMedToday

qXR from Qure.ai bags regulatory clearance - IndiaMedToday

Qure.ai's qXR, an AI-enabled chest X-ray tool, received CE Class IIb certification for children aged 0-3 years, extending its capabilities to cover the entire childhood spectrum for TB screening.

2025-10

FDA

K251934 - 510(k) Premarket Notification - FDA

This is an FDA 510(k) Premarket Notification for qXR-Detect by Qure.Ai Technologies, indicating its regulatory status and the applicant's contact information.

2026-08

Videos

Product demos, reviews, and walkthroughs for qXR-Detect.

qXR- Artificial Intelligence aided Tuberculosis detection

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AI in Action: Accelerating Lung Cancer Diagnosis with qXR

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qTrack for TB screening

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How AI on a Routine X-Ray Saved Lives | Early Lung Cancer Detection by Qure.ai

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qTrack for Lung Nodules - End-to-End Disease Management using Artificial Intelligence Algorithms

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Frequently Asked Questions

What is qXR-Detect and how does it integrate into my existing clinical workflow?

qXR-Detect is a computer-assisted detection (CADe) software that analyzes chest X-ray images to identify, highlight, and categorize suspicious regions of interest across six categories: lung, pleura, mediastinum/hila and heart, bone, hardware, and other findings. It is designed to integrate with existing radiology IT systems like PACS, viewers, and RIS, providing pre-read assistance and AI overlays to support radiologists and other physicians in interpreting chest X-rays.

Is qXR-Detect FDA cleared and what are its cleared indications?

Yes, qXR-Detect has received 510(k) Class II clearance from the U.S. FDA. It is cleared for the early identification, highlighting, and categorization of key positive findings on plain film chest X-rays to support emergency room physicians, family medicine practitioners, and radiologists. The software also has a Predetermined Change Control Plan (PCCP), allowing algorithm updates without requiring additional clearance.

What are the limitations of qXR-Detect, and should I rely solely on its findings for diagnosis?

qXR-Detect is intended as a support tool to assist clinician decision-making and improve efficiency and accuracy, not as a diagnostic device or a source of medical advice. Clinicians are responsible for viewing the original chest X-rays as per the standard of care and using the results in conjunction with other patient information and professional judgment. The device highlights suspicious regions for informational purposes only and does not alter the original medical image.

How accurate is qXR-Detect in identifying abnormalities, and what conditions can it detect?

qXR-Detect is trained on a large dataset of chest X-rays and has demonstrated high accuracy in detecting various abnormalities. It can detect and localize numerous findings, including lung nodules, cardiomegaly, pleural effusion, consolidation, fibrosis, and signs indicative of tuberculosis and COVID-19.

Are there alternative AI solutions for chest X-ray interpretation, and how does qXR-Detect compare?

Yes, there are other AI solutions for chest X-ray interpretation. For instance, in a comparative study for tuberculosis detection, Insight CXR (Lunit) and Nexus CXR (Nexus) showed slightly higher AUCs than qXR (Qure.ai). However, qXR-Detect is noted for its comprehensive regulatory clearances and its PCCP, which ensures access to up-to-date algorithms.

What is the typical pricing model for qXR-Detect, and how is it implemented in a healthcare setting?

Specific pricing details for qXR-Detect are not publicly available in the provided search results. However, Qure.ai's solutions are generally designed to be deployed on cloud or on-premise and integrate into existing radiology workflows, aiming to improve healthcare outcomes and costs.

How does qXR-Detect contribute to clinical decision support and improve patient outcomes?

qXR-Detect assists physicians by providing rapid pre-read assistance, highlighting potential abnormalities, and categorizing findings, which can lead to faster and more accurate diagnoses. This can help reduce reporting backlogs, improve turnaround times, and support earlier recognition of conditions like lung nodules and other abnormalities that may require further workup.