Qure.ai
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About
Qure.ai is a comprehensive health technology platform designed to enhance patient outcomes through deep learning and artificial intelligence in medical imaging. The software provides end-to-end support for identifying and managing critical conditions, specifically focusing on lung cancer, tuberculosis, and neurocritical care. By integrating directly into existing hospital infrastructure, the platform assists medical professionals in interpreting chest X-rays and CT scans with significantly increased speed and precision, impacting over 40 million lives annually across 107 countries. The suite consists of several specialized modules, including qXR for identifying subtle pulmonary nodules on radiographs and qCT LN Quant for automated volumetric segmentation on CT scans. For emergency scenarios, the qER solution provides rapid triage for conditions such as intracranial hemorrhage or pneumothorax, alerting physicians to critical pathology before manual reviews take place. Unlike traditional NLP tools that rely on rule-based processing, Qure.ai utilizes Large Language Models (LLMs) to extract full clinical context from electronic medical records (EMRs) and radiology reports, allowing for more nuanced risk stratification and follow-up recommendations. This platform is primarily built for radiologists, pulmonologists, and emergency physicians who face high caseloads and diagnostic fatigue. It is equally valuable for care coordination teams and hospital administrators who need to manage long-term patient pathways. By automating longitudinal tracking through the qTrack module, the system ensures that at-risk patients do not fall through the cracks of the healthcare system, while also reducing the administrative burden on clinical staff through AI-assisted workflows. What differentiates Qure.ai from other medical AI providers is its extensive regulatory validation, boasting 19 FDA-cleared indications and over 200 publications. The platform is designed not just for detection but for navigation and financial sustainability; many of its solutions are billable under CMS-issued CPT codes. This allows healthcare facilities to establish stable revenue models while adopting advanced technology that facilitates faster, more accurate diagnostics for life-changing conditions.
Pros & Cons
Holds 19 FDA-cleared indications for clinical use across multiple pathologies.
Reported sensitivity of up to 96% for identifying subtle chest radiography findings.
Supports billable CPT codes allowing hospitals to establish a stable revenue model.
Uses LLMs to extract context from EMRs rather than just matching keywords.
Unified platform that manages the entire pathway from detection to follow-up.
Requires specific alignment with revenue-cycle teams for successful reimbursement coding.
Clinical effectiveness for subtle nodules depends on specific imaging quality standards.
Full feature set requires integration across multiple hospital data systems.
Public pricing is not available and requires direct consultation with sales teams.
Use Cases
Emergency physicians can use qER to rapidly triage critical conditions like pneumothorax or brain bleeds before manual film review.
Radiologists can automate the volumetric segmentation of lung nodules on CT scans to improve diagnostic accuracy and save time.
Pulmonologists can track at-risk lung cancer patients longitudinally using qTrack to ensure timely follow-up imaging and biopsies.
Hospital administrators can implement AI workflows that qualify for CMS reimbursement codes to generate new revenue streams.
Global health organizations can deploy qXR for automated tuberculosis screening in regions with limited access to human readers.
Platform
Features
• intracranial hemorrhage detection
• risk stratification (lung-rads®)
• llm-based clinical context extraction
• emr and pacs integration
• longitudinal patient tracking
• emergency triage for pneumothorax
• ct quantification and volumetry
• lung nodule detection (qxr)
FAQs
What imaging modalities does Qure.ai support?
The platform supports chest radiographs (X-rays) for lung nodule and tuberculosis detection, as well as CT scans for quantification and tracking. It also includes specialized solutions for brain CT scans to identify intracranial hemorrhages.
Is Qure.ai FDA cleared?
Yes, Qure.ai holds 19 FDA-cleared indications for various medical conditions. This includes specific clearances for lung nodule detection on radiographs (qXR-LN) and advanced CT-based nodule quantification (qCT LN Quant).
Does the AI integrate with existing hospital systems?
The software integrates seamlessly with standard healthcare infrastructure including PACS, EMR, dictation, and local reporting systems. It is designed to accelerate interpretation without requiring clinicians to change their established workflows.
How does the platform assist with patient follow-up?
Through the qTrack module, the system automates longitudinal tracking of patients across imaging and follow-up appointments. It provides real-time status updates and care coordination to help ensure at-risk patients receive timely treatment.
Are these AI services billable for hospitals?
Yes, many of Qure.ai’s solutions are billable under CMS-issued CPT codes, such as 0721T and 0727T. The company works with revenue-cycle teams to align documentation and coding within existing workflows.
Pricing Plans
Enterprise
Unknown Price• 19 FDA-cleared indications
• qXR lung nodule detection
• qCT volumetric quantification
• qER emergency triage
• qTrack longitudinal follow-up
• PACS and EMR integration
• Billable under CPT codes
• LLM clinical context analysis
• Real-time care coordination
• Global implementation support
Job Opportunities
Sr. Fullstack Engineer
Empower radiologists and clinicians with FDA-cleared AI for early disease detection, automated imaging quantification, and longitudinal patient follow-up care.
Benefits:
Competitive compensation and rewards
Opportunity to grow with a young company
Inclusive workspace
Education Requirements:
Bachelor's degree in computer science or a related field
Experience Requirements:
4-8 Years
At-least 4 years of working experience in full-stack development
Other Requirements:
Expertise in Django, React, Python, JavaScript, TypeScript
Knowledge of Python and JavaScript
Experience with designing RESTful APIs
Experience with database technologies such as PostgreSQL and Redis
Familiarity with CI/CD pipelines, Docker, and Cloud Servers
Responsibilities:
Utilize Django and Python for back-end development
Work with cross-functional teams including data scientists and product managers
Design and implement efficient APIs
Ensure code quality and conduct code reviews
Optimize applications for maximum speed and scalability
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Data Engineer
Empower radiologists and clinicians with FDA-cleared AI for early disease detection, automated imaging quantification, and longitudinal patient follow-up care.
Benefits:
Competitive salary
Comprehensive benefits
Professional growth and leadership opportunities
Supportive and fast-paced environment
Experience Requirements:
3+ years of experience in data engineering, analytics, or BI-focused roles
4-5 years
Other Requirements:
Strong proficiency in SQL and NoSQL databases
Experience with Metabase, Mixpanel, or similar BI tools
Proficiency in Python and experience with ETL frameworks
Understanding of data modeling and business intelligence best practices
Responsibilities:
Develop and optimize ETL processes
Manage and enhance BI tools like Metabase
Build and maintain dashboards and data models
Work with Mixpanel to analyze user behavior
Ensure data quality, security, and governance
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LLM Engineer
Empower radiologists and clinicians with FDA-cleared AI for early disease detection, automated imaging quantification, and longitudinal patient follow-up care.
Benefits:
Opportunity to work alongside the best minds in the industry
Education Requirements:
Bachelor’s degree in Computer Science, Information Technology, or a related field
Experience Requirements:
2-5 years
4-5 years
Other Requirements:
Solid understanding of LLM architectures
Strong programming skills in Python
Experience integrating OpenAI, Anthropic, or open-source models
Familiarity with emerging LLM protocols such as MCP
Comfort with cloud platforms (AWS/GCP) and Docker/K8s
Responsibilities:
Design and implement LLM-based features across Qure’s products
Build internal tools, APIs, and developer-facing utilities
Collaborate on modular workflows (RAG, chaining, structured prompting)
Evaluate model performance and behavior using human feedback and metrics
Optimize latency, cost, and reliability of LLM systems
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Ratings & Reviews
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