AI-powered medical imaging for tuberculosis, lung cancer, and stroke care
Qure.ai is a medical imaging AI platform for radiology-based diagnosis and care coordination in healthcare systems.
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Qure.ai's products are deployed within clinical imaging workflows, where they process X-rays and CT scans to flag abnormalities and support clinician decision-making. For tuberculosis, the qXR tool screens chest X-rays for signs of TB, silicosis, and pediatric TB, feeding into disease surveillance systems used by public health programs. For lung cancer, the platform tracks nodules over time to measure disease progression across a patient's care pathway, from initial detection through to case management. For stroke, a care coordination suite triages patients and connects hub and spoke hospitals in real time so clinicians can act within the critical treatment window.
The website highlights that its AI models are designed to work with a range of imaging hardware, including older X-ray machines, which it says allows healthcare facilities to use existing equipment rather than requiring new infrastructure. Qure.ai references WHO evaluations of its TB screening tools, FDA clearances for some of its imaging AI products, and real-world studies measuring clinical impact, such as a published study on stroke treatment speed. It also describes work on AI biomarkers intended for use in clinical trials, in addition to its point-of-care diagnostic tools.
Qure.ai's customers include hospitals, national tuberculosis programs, ministries of health, and pharmaceutical companies. Cited partners and clients include the NHS (Greater Glasgow and Clyde, and Nottingham University Hospitals), Medtronic, AstraZeneca, Merck's Global Health Innovation Fund, Medica Group, and Lesotho's Ministry of Health. Pricing is not published on the website and is arranged directly with healthcare providers, health systems, and government programs; the product falls in the broader category of medical imaging AI alongside vendors such as Aidoc and Viz.ai.
qER is a radiological computer-aided triage and notification tool that flags suspected intracranial hemorrhage, mass effect, midline shift, and cranial fracture on head CT images to accelerate emergency care.
Qure.ai's qXR uses deep learning to classify chest X-rays as normal or abnormal and detect abnormalities in the lungs, pleura, mediastinum, bones, diaphragm, and heart in under a minute.
qXR-BT is FDA 510(k) cleared automated radiological image processing software that facilitates confirmation of breathing tube position relative to anatomical landmarks on adult chest X-rays.
qXR-LN is FDA 510(k) cleared computer-aided detection and localization software that identifies and marks regions suspicious for pulmonary nodules on frontal chest radiographs as a second reader.
qCT LN Quant provides advanced CT-based nodule quantification and tracking, including Lung-RADS and Brock score risk stratification with Fleischner guideline-based follow-up recommendations.
qER-Quant automatically labels, visualizes, and quantifies segmentable brain structures—intracranial hyperdensities, lateral ventricles, and midline shift—from non-contrast head CT images.
Qure.ai's Stroke Care Suite combines the qER algorithm with the Qure App to send high-priority AI-powered notifications that activate specialist teams as soon as a stroke is detected.
qTrack is an end-to-end disease management platform that gives stakeholders ready access to patient information, diagnosis tracking, test results, and real-time progression monitoring across departments.
Qure.ai onboarded its lung cancer workflow suite, including qXR-LN, qCT-LN Quant, qTrack, and qER, onto Microsoft's Precision Imaging Network for streamlined single-point deployment to U.S. hospitals.
qXR integrates directly into the radiology workflow, pushing visual and free-text outputs back to PACS and sending HL7 messages to the RIS for reporting radiologists to review.
The Qure.ai app is a cloud-based image viewing application supporting multi-modality CT, MR, and X-ray review on mobile and desktop devices, including HIPAA-compliant messaging and critical patient alerting.
Multiple Qure.ai products—including qXR-LN, qER (V2.0), qER-Quant, qCT-Quant, qXR-BT, qXR-PTX-PE, and qXR-CTR—are US FDA 510(k) cleared and CE certified for clinical use.
Qure.ai does not publish list pricing; it sells its AI radiology solutions (e.g., qXR, qER, qCT, qTrack) directly to hospitals, health systems, and government/enterprise buyers via custom, sales-led contracts, typically priced per scan volume, per site, or as an enterprise license negotiated with the vendor.
FDA-cleared imaging AI with real regulatory depth, but you'll never see a price until legal gets involved.
“Qure.ai stacks multiple FDA 510(k) clearances and WHO evaluations across TB, lung cancer, and stroke tools. Pricing is fully custom, so speed to contract depends entirely on your procurement team.”
Seven FDA-cleared products. qXR, qER, qCT-Quant, qXR-LN. That's not a startup with one demo, that's a company that's been through the regulatory wringer repeatedly.
Two things stand out. One: it works with old X-ray hardware, so hospitals skip capital spend. Two: it's on Microsoft's Precision Imaging Network for deployment, which suggests real integration muscle, not just an API doc.
No pricing page, no free trial, sales-led only. Fine for a hospital system, painful if you're a smaller clinic wanting to test before a six-month procurement cycle. Aidoc and Viz.ai play the same field — Qure.ai's edge is breadth across TB, lung cancer, and stroke rather than single-condition depth. Pilot at one site, measure turnaround time, then negotiate enterprise terms.
Breadth across TB, lung cancer, and stroke in one platform, plus Microsoft Precision Imaging Network deployment, differentiates from single-condition tools.
FDA/CE clearances de-risk clinical use, but no published data portability or contract terms anywhere on the site.
Works with existing X-ray hardware per their own Q&A, cutting infrastructure lead time, but sales-led onboarding adds friction.
Detects conditions clinicians can't reliably catch at scale manually — this is new capability, not just cheaper reads.
Multiple FDA 510(k) clearances and CE certifications across 7+ named products signal active, sustained regulatory shipping.
Hospital systems or national health programs needing multi-condition imaging AI with regulatory clearance already in hand.
Skip if you need transparent per-seat pricing or a self-serve trial before looping in procurement.
Regulatory-grade radiology AI with a three-disease focus that matches how public health systems actually triage risk.
“Qure.ai's FDA-cleared, WHO-evaluated portfolio spans TB, lung cancer, and stroke with real workflow integration, not a single-point algorithm demo. The gap is pricing opacity, which matters when you're building a multi-year capital plan.”
qXR-LN and qER carry FDA 510(k) clearance, not just a CE mark, and the WHO evaluation on TB screening tells me their evidence pipeline is built for regulator scrutiny, not just a validation paper. That's the difference between a pilot tool and something I can put in front of my radiology chiefs and public health partners without a fight.
The hub-and-spoke stroke suite and qTrack care coordination platform show they understand that imaging AI is worthless if it doesn't change what happens in the next 20 minutes of clinical workflow. Compatibility with older X-ray machines matters enormously for TB programs in resource-constrained settings — that's the deployment reality most vendors ignore.
My hesitation: no published pricing, sales-led contracts only, and per-scan-volume terms I can't model until legal is already in the room. Deployment across 5,500+ sites in 105+ countries suggests real staying power, but locking a health system into qXR, qER, and qCT as one bundled workflow is a three-year architectural commitment I'd want capped and reviewed annually.
Sits alongside Aidoc and Viz.ai in imaging AI but differentiates on TB and global health program deployment rather than U.S. acute-care focus alone.
TB, lung cancer, and stroke coverage plus old-hardware compatibility matches actual public health and hospital triage patterns.
PACS/RIS push-back, HL7 messaging, and Microsoft Precision Imaging Network onboarding show real workflow embedding, not a standalone viewer.
5,500+ site deployment signals durability, but bundled multi-tool adoption is a multi-year commitment without published exit terms.
FDA clearances across qXR-LN, qER, qCT-Quant, and qXR-BT show regulatory depth beyond a single flagship algorithm.
Health systems and national TB or stroke programs needing regulatory-cleared imaging AI that works with existing hardware.
You need transparent, self-serve pricing to benchmark against other imaging AI vendors before engaging procurement.
No price on the site. Zero. Everything's a call with sales.
“Qure.ai sells qXR, qER, and qTrack via custom enterprise contracts, not list price. Strong regulatory story — FDA 510(k), CE, WHO evaluation — but the finance model is a black box.”
No pricing page. Contact sales, per the site — that's the whole model. Deployed at 5,500+ sites in 105+ countries, per their own numbers, but volume doesn't tell you the invoice.
Pricing is per-scan-volume or per-site, negotiated. That means TCO at year 3 depends entirely on scan growth you don't control. A hospital scaling CT volume 20% a year could see costs climb without a published overage rate — the real risk isn't the contract price, it's the usage curve.
Competitors named in the category — Aidoc, Viz.ai — also sell sales-led, so this isn't unusual. It's category norm, not a red flag specific to Qure.ai. But procurement teams should budget for PACS integration costs and multi-site negotiation time. The portfolio spans TB, lung cancer, and stroke across seven FDA-cleared products — bundling across them might be the actual savings lever, not list price.
Enterprise sales-led onboarding across hospitals, ministries of health, and pharma implies real procurement cycles, not self-serve.
No renewal or term-length terms published; evidence silent, category norm is multi-year enterprise lock-in.
No published pricing; site confirms custom contracts only, per-scan or per-site.
Cited real-world stroke treatment-speed study and WHO evaluation give measurable clinical anchors, unusual for this category.
Volume-based pricing means 3-year cost scales with scan growth — hard to forecast without a rate card.
Hospital systems and national health programs that can run a multi-month procurement cycle.
You need a quotable price before looping in finance.
PACS-native triage AI with real FDA clearances, but the on-call radiologist still can't see a price sheet.
“qXR and qER slot into existing PACS/RIS workflows and read on old X-ray hardware, which matters in TB programs and stroke networks with no capital budget for new machines. The gap: no published pricing, no docs portal, no sandbox to test drive before your IT team signs a contract.”
qXR reads chest films in under a minute and pushes HL7 back to the RIS. That's the right integration point — nobody wants a separate viewer during a night shift. qER flags intracranial hemorrhage and midline shift on head CT and fires alerts through the Qure App to activate stroke teams; the workflow mirrors how a hub-and-spoke stroke network actually escalates, not a bolted-on dashboard.
Day-3 reality depends entirely on deployment terms you negotiate up front — cloud vs on-prem, PACS integration scope — none of which is visible pre-sale. No pricing page, no docs site, no API reference anywhere on the site. That's normal for enterprise radiology AI sold to ministries of health and hospital systems, but it means your first real look at the interface comes after procurement, not before.
FDA 510(k) clearance on qXR-LN, qXR-BT, qER v2.0 gives compliance teams something concrete. Second-reader nodule detection plus Lung-RADS/Brock scoring in qCT-Quant is genuinely useful for tumor boards tracking nodules over years, not just a single scan.
Sub-minute chest X-ray triage and automated stroke alerts suggest real workflow speed once live, but pre-sale trial access is absent from evidence.
No docs portal or API reference anywhere on the site — navigation runs Products, Impact, Evidence, Insights, Contact Us, with nothing for integration teams.
Custom sales-led contracts and no published pricing mean procurement friction even if in-workflow friction is low.
qCT-Quant's Lung-RADS/Brock stratification and qER-Quant's brain structure quantification go beyond flag-and-forget triage into longitudinal case management.
Direct PACS/RIS push via HL7 and Microsoft Precision Imaging Network onboarding show it's built to sit inside existing radiology infrastructure, not replace it.
National TB programs, stroke networks, and health systems that can run a sales-led enterprise procurement process.
You need to self-serve a trial or read integration docs before looping in procurement.
This isn't a dashboard you log into, it's a workflow bolted into your radiology department.
“Qure.ai lives inside PACS and hospital pipelines, not in a browser tab you check daily. That makes normal software-review questions like onboarding and mobile parity almost beside the point.”
Most of what I judge — empty states, first ten minutes, spinners — barely applies here. This is qXR reading chest X-rays in under a minute, qER flagging brain bleeds, qTrack coordinating stroke cases across hub-and-spoke hospitals. It sits inside PACS and RIS, pushes HL7 messages, and gets used by radiologists mid-shift, not evaluated by someone poking around a free trial. There isn't one.
What's real: FDA 510(k) clearance on several products, WHO evaluation on the TB tools, deployment across 5500+ sites in 105+ countries per their own numbers. That's not demo glow, that's regulatory paperwork you can't fake.
The tradeoff: no published pricing, no trial, sales-led contracts only, so you're buying on trust and reference calls, not hands-on testing like you'd get with a Viz.ai or Aidoc eval. Fine for a hospital system with a procurement team. Rough if you just want to try it.
Integrates directly into PACS/RIS workflow rather than a standalone app, which is the right kind of unglamorous.
Designed to work with existing older X-ray hardware, lowering the infrastructure learning curve, though clinical adoption still needs workflow training.
Dedicated mobile app supports multi-modality CT/MR/X-ray review with HIPAA-compliant alerting, real functionality not just a companion viewer.
No trial, no docs page listed, sales-led custom contracts mean onboarding is a procurement process, not a ten-minute test drive.
FDA 510(k) clearance and CE certification across multiple products (qXR-LN, qER V2.0, qCT-Quant) is a stronger reliability signal than any spinner or autosave test.
Hospital systems, national TB programs, or health ministries needing regulatory-cleared imaging AI at scale.
You want to self-serve test a tool with a free trial before involving procurement.
Seven FDA clearances you can look up, and a 45M-lives counter with no auditor named.
“Seven FDA 510(k) clearances and WHO-evaluated TB screening give this a regulatory paper trail most imaging AI can't produce. The adoption figures under the H1 are specific and public, but every one of them is self-reported.”
Start with what someone other than the vendor signed off on. qXR-LN, qER V2.0, qER-Quant, qCT-Quant, qXR-BT, qXR-PTX-PE, qXR-CTR — seven products, FDA 510(k) cleared, CE certified. WHO evaluation on the TB screening. That's an external check, and most imaging AI can't show one.
The counters under the H1 are a different animal. 45M+ lives, 105+ countries, 5,500+ sites. Real numbers, not fog — but self-reported, with no auditor named. Category norm across imaging AI, Aidoc and Viz.ai included. Still the line I'd want verified in the contract.
Exit is the harder problem. qXR pushes into PACS and sends HL7 to the RIS, so removal is a workflow rebuild, not a cancellation. No changelog, no docs portal, no API reference to check release cadence against. I'd ask for an SLA and a data-export clause before a multi-year term.
TB, lung-nodule and stroke coverage in one portfolio is broader than the single-purpose triage products it sits beside.
qXR pushes into PACS and sends HL7 to the RIS, so removal is a workflow rebuild rather than a cancellation.
Named NHS, Medtronic and AstraZeneca engagements suggest durable installs; no changelog, docs portal or API reference to gauge release cadence.
H1 superlative is backed by on-page figures (45M+ lives, 105+ countries, 5,500+ sites), but they're self-reported; FDA/WHO/CE claims are specific and checkable.
Seven FDA 510(k)-cleared products plus WHO-evaluated TB screening back the clinical claims with outside sign-off.
Health systems that need FDA-cleared imaging AI running inside their existing PACS workflow.
Buyers who need independently audited deployment numbers before signing a multi-year contract.
Common questions answered by our AI research team
Yes. Qure.ai's technology fits all kinds of X-ray machines, new or old, letting facilities use existing hardware while getting immediate AI-driven results for conditions like TB.
Qure.ai's AI detects and manages tuberculosis, pediatric tuberculosis, silicosis, lung cancer, and stroke, using chest X-rays and CT scans across a global network of 5500+ sites in 105+ countries.
Yes. Qure.ai offers WHO-evaluated AI solutions supporting clinicians in fast, accurate diagnosis and treatment decisions for Tuberculosis, Silicosis, and Pediatric Tuberculosis, along with real-time disease surveillance.
Yes. Its end-to-end Lung Cancer care continuum detects lung nodules early, measures disease progression over time, and helps manage cases to improve patient outcomes.
Qure.ai provides an AI-powered care coordination suite enabling patient triage and real-time communication across Hub & Spoke hospital networks, helping clinicians deliver timely stroke interventions anywhere.
Qure.ai is a Mumbai-based company that develops AI software for interpreting radiology scans, including chest X-rays and head CT scans.
